{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nSnowflake is building the most powerful AI Data Cloud in the world — and Canada is a critical part of that mission. Our AI/ML engineering teams are delivering the intelligence layer that powers Cortex AI, large language model inference, AI functions, intelligent search, and the next generation of AI-powered experiences on the Snowflake platform.\n\nWe're looking for a Software Engineer — AI to join our growing Toronto engineering team. You'll design, build, and ship AI-powered systems and services that are used by thousands of organizations worldwide. If you're passionate about building production AI infrastructure at scale and want to work alongside world-class engineers on problems that matter, this is the role.\n\n\nAS A SOFTWARE ENGINEER — AI, YOU WILL:\n\n - Design, develop, and ship AI-powered features and services — working across model inference, AI functions, retrieval systems, and intelligent application experiences on the Snowflake platform\n\n - Build production-quality infrastructure for AI workloads — including model serving, embedding pipelines, retrieval-augmented generation (RAG), and AI-driven automation\n\n - Write clean, performant, and well-tested code in languages like Python, Java, C++, or Go — contributing to systems that operate at enterprise scale across Snowflake's multi-cloud infrastructure\n\n - Collaborate with ML researchers, senior engineers, and product managers to translate AI research and product requirements into robust, scalable engineering solutions\n\n - Contribute to system design — participate in design reviews, propose architectural improvements, and help shape the technical direction of AI services\n\n - Own your work end-to-end — from design and implementation through testing, deployment, monitoring, and operational health\n\n - Stay current on AI/ML advancements — evaluate new techniques, models, and tools and assess their applicability to Snowflake's platform and customer needs\n\n - Contribute to a strong engineering culture — through code reviews, documentation, knowledge sharing, and continuous improvement of team practices\n\n\nOUR IDEAL SOFTWARE ENGINEER — AI WILL HAVE:\n\nRequired:\n\n - 3+ years of software engineering experience building production systems, with a strong foundation in computer science fundamentals (data structures, algorithms, distributed systems)\n\n - Experience with AI/ML systems — you've worked on model serving, inference pipelines, LLM integration, embedding systems, or AI-powered application features in a production environment\n\n - Proficiency in one or more of: Python, Java, C++, or Go\n\n - Experience building distributed systems or cloud-native services — you understand the challenges of scale, reliability, and performance in a multi-cloud environment\n\n - Strong problem-solving skills and the ability to break down ambiguous problems into clear engineering tasks\n\n - Collaborative mindset — you work effectively with engineers, researchers, and product managers and communicate your ideas clearly\n\n - Ownership mentality — you take responsibility for your work from design through production and care about quality, reliability, and operational excellence\n\nPreferred:\n\n - Experience with LLM inference, fine-tuning, RAG architectures, or vector search at production scale\n\n - Familiarity with ML frameworks such as PyTorch, TensorFlow, or similar\n\n - Experience with cloud infrastructure (AWS, Azure, or GCP) and containerization (Docker, Kubernetes)\n\n - Background in data platforms, data warehousing, or SQL-based systems\n\n - Experience with CI/CD pipelines, automated testing, and production monitoring/observability\n\n - Contributions to open-source AI/ML projects or published research in AI/ML\n\n\nSNOWFLAKE VALUES WE'RE LOOKING FOR:\n\n - Get It Done — You ship working software. You move fast, iterate, and deliver results — not just prototypes.\n\n - Own It — You take end-to-end responsibility for your code, your systems, and your outcomes. If something breaks, you fix it.\n\n - Make Each Other the Best — You contribute to a culture of learning through thoughtful code reviews, knowledge sharing, and helping teammates grow.\n\n - Think Big — You don't just solve today's problem. You think about how AI at Snowflake should work at 10x scale and buil","salary_min":170000,"salary_max":500000,"location":"Toronto, Canada","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["rag","mlops","pytorch","fine-tuning","cloud","tensorflow","agents","llm"],"apply_url":"https://jobs.ashbyhq.com/snowflake/628f36e0-8554-4d5f-8d5e-a017a761ca54/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T00:00:58.144Z","expires_at":"2026-11-13T16:59:18.383644Z","created_at":"2026-10-14T15:22:50.102644Z","updated_at":"2026-10-14T16:59:18.386616Z","company_name":"Snowflake","company_slug":"snowflake","company_logo_url":"https://www.google.com/s2/favicons?domain=snowflake.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8bf0267f-9f49-4c14-b3ad-dfe54d410a18"},{"id":"e134d960-5a8f-48f6-a055-10527e302063","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (APAC)","slug":"solution-architect-apac-7456acbf","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE:\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\n\n\nKEY RESPONSIBILITIES:\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect, Deployed Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communica","salary_min":185000,"salary_max":260000,"location":"Singapore","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["llm","rag","api-design","agents"],"apply_url":"https://jobs.ashbyhq.com/langchain/c882af7e-59aa-4017-b791-9a8094c0ef6f/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-10T16:57:17.138Z","expires_at":"2026-11-13T16:55:08.058735Z","created_at":"2026-08-25T18:26:42.877158Z","updated_at":"2026-10-14T16:55:08.06149Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e134d960-5a8f-48f6-a055-10527e302063"},{"id":"a6cb1a62-a2ae-4b86-bb0a-c7210c241a8f","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (NYC)","slug":"solution-architect-nyc-299c723c","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\nKEY RESPONSIBILITIES\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communication skills with abilit","salary_min":170000,"salary_max":300000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","agents","api-design","rag"],"apply_url":"https://jobs.ashbyhq.com/langchain/f71210f9-12e1-4726-88b9-ebafa194d5b2/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:09:49.395Z","expires_at":"2026-11-13T16:55:07.987572Z","created_at":"2026-08-25T18:26:42.786487Z","updated_at":"2026-10-14T16:55:07.990281Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a6cb1a62-a2ae-4b86-bb0a-c7210c241a8f"},{"id":"33db21e5-9d88-4de6-b2d0-3c134c649a65","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (Austin)","slug":"solution-architect-austin-318d756c","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\nKEY RESPONSIBILITIES\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communication skills with abilit","salary_min":170000,"salary_max":270000,"location":"Austin, TX","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["llm","api-design","agents","rag"],"apply_url":"https://jobs.ashbyhq.com/langchain/da7cbabd-ceed-4d7d-ae25-f226cac0c1c1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:09:36.459Z","expires_at":"2026-11-13T16:55:07.99297Z","created_at":"2026-08-25T18:26:42.792069Z","updated_at":"2026-10-14T16:55:07.996563Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/33db21e5-9d88-4de6-b2d0-3c134c649a65"},{"id":"f98249b2-813f-48ef-91e4-d2e791004bd2","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (Dallas)","slug":"solution-architect-dallas-51e1819a","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\nKEY RESPONSIBILITIES\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communication skills with abilit","salary_min":170000,"salary_max":270000,"location":"Dallas, TX","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["api-design","rag","llm","agents"],"apply_url":"https://jobs.ashbyhq.com/langchain/933a41a3-43ca-44de-a0ae-f541149151b7/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:09:25.388Z","expires_at":"2026-11-13T16:55:07.999911Z","created_at":"2026-08-25T18:26:42.797861Z","updated_at":"2026-10-14T16:55:08.003316Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f98249b2-813f-48ef-91e4-d2e791004bd2"},{"id":"c373a55c-edc7-4941-8d08-2344dd5aecb8","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (Remote)","slug":"solution-architect-remote-1a61487f","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communication skills with abil","salary_min":170000,"salary_max":270000,"location":"Los Angeles, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["rag","api-design","agents","llm"],"apply_url":"https://jobs.ashbyhq.com/langchain/0a5dd30c-6da1-4095-bd96-b16f27eeb333/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:09:15.311Z","expires_at":"2026-11-13T16:55:08.022981Z","created_at":"2026-08-25T18:26:42.82559Z","updated_at":"2026-10-14T16:55:08.026028Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c373a55c-edc7-4941-8d08-2344dd5aecb8"},{"id":"9db2d343-2be2-476f-8816-846f2bbf743b","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (San Francisco)","slug":"solution-architect-san-francisco-576342e0","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communication skills with abil","salary_min":170000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["api-design","agents","rag","llm"],"apply_url":"https://jobs.ashbyhq.com/langchain/e12d7176-8b9f-438a-8f3b-0cbbffab9c5c/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:08:47.814Z","expires_at":"2026-11-13T16:55:07.907327Z","created_at":"2026-08-25T18:26:42.712816Z","updated_at":"2026-10-14T16:55:07.910314Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9db2d343-2be2-476f-8816-846f2bbf743b"},{"id":"7700aa2a-0736-4261-86f4-43e527ee6f2c","company_id":"b4787255-dacd-444b-8e44-bb9971ec1f36","title":"Senior Director, Product Management, Agentic Platform","slug":"senior-director-product-management-agentic-platform-368b55e9","description":"ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.\n \n ZoomInfo has rebuilt itself around AI, and every agentic experience we ship runs on one shared platform — Copilot chat, agent teams, bots, GTM Studio, MarketingOS, and products that don't exist yet. As Senior Director of Product Management for the Agentic Platform, you'll be the right hand to our VP of Agents \u0026 Orchestration, owning the core primitives every agent depends on and the forward-looking R\u0026D that keeps our agents ahead of what off-the-shelf models and frameworks can do. The best practices for agentic platforms are being written right now, and you'll help write them.\n What You'll Do \n \n You will set the multi-year Agentic Platform strategy and turn it into a roadmap that balances reliability, speed for product teams, and bets on the frontier.\n You will own core platform primitives — multi-agent orchestration, planning and execution runtimes, memory and context management, tool and MCP infrastructure, guardrails, and observability — and define the abstractions that let product teams compose new agents quickly.\n You will lead the product side of custom model training and fine-tuning on ZoomInfo's proprietary data, deciding where custom models create lasting advantage versus frontier models.\n You will build the decisioning-model layer that lets agents choose the right action, partnering with data science to turn ZoomInfo's signals into models agents invoke as first-class capabilities.\n You will run a structured orchestration R\u0026D program that tracks emerging research and moves the winners into production.\n You will identify and ship net-new platform capabilities that unlock new product surfaces and revenue, taking ideas from whiteboard sketch to production primitive.\n You will partner with engineering to make agent infrastructure production-grade — measurable on execution at scale, latency, cost per task, security, and compliance.\n You will build and grow a team of senior and principal PMs, hiring for technical depth and ownership and developing leaders who run their own areas with the same bar you hold.\n \n What You Bring \n Must-Have: \n \n At least 10 years building technical products, including at least 3 years leading teams of PMs or running a company\n Deep, hands-on experience with production agentic systems — agents that plan, invoke tools, and take real actions, not just chat interfaces\n A track record of taking an AI or agentic product from 0→1, either as a founder or as the product leader who owned it end to end\n Fluency across the modern agentic stack, including orchestration frameworks (LangGraph, CrewAI, or custom), MCP and tool-calling patterns, RAG and memory architectures, and evals and observability\n Enough technical depth to review architecture docs, read agent traces, and prototype in code yourself\n \n Preferred: \n \n Working knowledge of model development, including fine-tuning, post-training, evaluation design, and the economics of custom versus frontier models\n Experience building platform products where engineering and product teams are your customers\n Experience shipping decisioning, ranking, or recommendation models into production\n Experience in B2B, GTM, or data platform businesses\n \n  \n  \n #LI-Remote\n #LI-VC1\n Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.\n In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here .\n Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.\n $192,920 — $303,160 USD \n About us:  \n ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.\n ZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.\n ZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available here .\n ZoomInfo is proud to be an equal opportunity employer, hiring based","salary_min":192920,"salary_max":303160,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["rag","code-generation","fine-tuning","agents"],"apply_url":"https://www.zoominfo.com/careers?gh_jid=8876309002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T17:48:12Z","expires_at":"2026-11-13T17:03:21.855103Z","created_at":"2026-10-14T15:25:25.418534Z","updated_at":"2026-10-14T17:03:21.859287Z","company_name":"ZoomInfo","company_slug":"zoominfo","company_logo_url":"https://www.google.com/s2/favicons?domain=zoominfo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7700aa2a-0736-4261-86f4-43e527ee6f2c"},{"id":"56ebdfff-97fb-41ef-a385-6ed65f0a35ea","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Staff Applied AI Engineer - Enterprise AI Solutions","slug":"staff-applied-ai-engineer-enterprise-ai-solutions-9fe2164f","description":"About Snorkel \n Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes.  Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n The Role\n As an Applied AI Engineer, you’ll research and utilize state-of-the-art Gen AI and machine learning (ML) techniques to successfully deliver solutions to our customers. You will work directly with our customers to understand their business and technical needs and design and deliver AI solutions to solve them. You will also help define Snorkel’s Applied AI tooling by translating repeatable real-world challenges into reusable solution recipes, workflows, best practices, and platform-level capabilities that become part of Snorkel’s next generation of AI tooling. We move fast and are constantly prototyping and innovating new ways to deliver value to our customers. This position is ideal for someone who enjoys solving complex problems, bridging the gap between AI technology and business value, working directly with customers, keeping up-to date with AI research, and standardizing bespoke solutions into internal recipes and staying naturally curious about the infrastructure that underpin the Applied AI stack end-to-end.\n Main Responsibilities\n \n Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. This will involve designing custom approaches using state-of-the-art tools, with the goal of delivering real business value and informing the evolution of Snorkel’s tooling.\n Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes and agentic workflows.\n Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. A data- and evaluation-first mindset is essential for success in this role.\n Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects.\n Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup.\n Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices.\n Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best-suited for Snorkel AI.\n Serve as the voice of our customers for new AI paradigms, data science workflows, and share customer feedback to product teams.\n Conduct one-to-few and one-to-many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI.\n Annual travel up to 25%.\n \n Preferred Qualifications\n \n B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience.\n 8+ years of customer-facing experience in the design and implementation of AI/ML solutions.\n Proficiency in Python, including strong grounding in software engineering fundamentals (e.g., modular design, testing, profiling, packaging) and experience with modern Python constructs and libraries for type validation and typed data modeling (e.g., pydantic), building type-safe systems (e.g., mypy), testing (e.g., pytest), packaging and environment configuration (e.g., poetry), API and service frameworks (e.g., FastAPI), serialization and structured data handling (e.g., msgspec), and orchestration tooling relevant to ML deployment (e.g., Ray, Airflow).\n Expertise across the Applied AI stack, spanning classical ML libraries (e.g., scikit-learn), deep learning frameworks (e.g., PyTorch), foundation-model ecosystems (e.g., Hugging Face Transformers), vector/embedding tooling (e.g., FAISS), data processing frameworks (e.g., pandas, Spark), retrieval/RAG tooling (e.g., Chroma, Weaviate), synthetic dataset curation, evaluation workflows, and LLM orchestration, workflow, agent authoring tools (e.g., LlamaIndex, LangGraph, CrewAI).\n Experience leading strategic, customer-facing initiatives and collaborating with business stakeholders to ensure ML solutions drive successful business outcomes, with a strong focus on teaching and enablement.\n Outstanding presentation skills to technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos.\n Ability to work in a fast-paced environment and balance priorities across multiple projects at once.\n \n Co","salary_min":230000,"salary_max":360000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","embeddings","pytorch","deep-learning","fine-tuning","agents","generative-ai","rag"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6204368004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T03:25:50Z","expires_at":"2026-11-13T16:55:33.58289Z","created_at":"2026-10-14T15:19:23.699153Z","updated_at":"2026-10-14T16:55:33.585681Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/56ebdfff-97fb-41ef-a385-6ed65f0a35ea"},{"id":"42d0b3d6-fb09-4db9-9c43-b451572a2005","company_id":"da5cfe83-4fb2-4ab3-9392-94069a77ae59","title":"Staff/Senior Staff Software Engineer, Agentic Search","slug":"senior-staff-software-engineer-agentic-search-c4676bc8","description":"Build AI contracting at enterprise scale. Ship it fast.\n\nIronclad is building AI on one of the most consequential datasets in enterprise software: over 2 billion contracts across thousands of the world's largest organizations. Contracts run every dollar, deal, and business relationship on the planet, and the AI transformation of this space is still largely uncharted. Engineers here are building agentic solutions to problems that haven't been solved yet, in a category that's still being defined. The feedback loop is short, the data is rich, and the people closest to the work are the ones deciding what gets built next. That ability to deliver impact quickly is why the the companies at the forefront of AI choose Ironclad to manage their contracts.\n\nWe’re consistently recognized as a leader in the industry: a Leader in the Forrester Wave and Gartner Magic Quadrant for Contract Lifecycle Management, a Fortune Great Place to Work, and one of Fast Company’s Most Innovative Workplaces. Ironclad has also been named to Forbes’ AI 50 and Business Insider’s list of Companies to Bet Your Career On. We’re backed by leading investors including Accel, Y Combinator, Sequoia, BOND, and Franklin Templeton. For more information, visit www.ironcladapp.com http://www.ironcladapp.com or follow us on LinkedIn.\n\n\n\n\nABOUT THE ROLE\n\nIronclad's Intelligence Platform team owns Agent Assistant, Conversational Search, and Content Understanding — the systems that help customers and AI agents understand, find, and act on the right contract information. These are the flagship AI capabilities of our product, built and operated by a combined team of ML and ML infrastructure engineers.\n\nWe have multiple roles open, and are hiring a range of levels — Staff and Senior Staff. As a Staff or Senior Staff Engineer, Agentic Search, you'll own the architecture that combines LLMs and retrieval systems to answer complex, ambiguous questions about a customer's contracts, and you'll set the technical direction that other engineers across the AI organization build on. You'll partner closely with product, applied science, and engineering leaders to raise the company's search quality bar, and you'll bring the technical depth and eval-driven rigor to turn ambiguous problems into shipped, measurable improvements. Scope and ownership will be calibrated to level.\n\n\n\n\nWHAT YOU'LL DO\n\n - Own agentic search architecture. Design and evolve the systems that combine LLMs and retrieval to produce optimal answers to complex or ambiguous questions.\n\n - Drive eval-driven development. Design and run the benchmarks and experiments that measure search quality, and use that feedback to continuously improve the system.\n\n - Raise the search quality bar. Contribute to and influence the company's overall search quality standard.\n\n - Own content understanding and ingestion. Turn raw documents into processed data that retrieval systems can consume, by building and using NLP/LLM models and pipelines.\n\n - Set technical direction. Define architectural decisions and technical direction that other engineers across the AI organization build on.\n\n\nQUALIFICATIONS\n\n - 10+ years building production systems, with a substantial portion in search, information retrieval, content understanding, or recommendation systems at meaningful scale.\n\n - Demonstrated depth in one of: learned/hybrid retrieval (lexical + vector + reranking), query understanding/NLU pipelines, or production LLM agent systems — ideally more than one.\n\n - Experience with search frameworks (Elasticsearch or equivalent — Solr, Vespa, OpenSearch; embedding search) in production, including relevance tuning and reranking.\n\n - Fluency with modern LLM APIs and multi-provider orchestration (Anthropic, OpenAI, Google) — reasoning about token budgets, provider-specific tool-calling semantics, and prompt-caching trade-offs.\n\n - Experience building eval-driven workflows — offline benchmarks, regression detection, structured A/B comparison — as opposed to shipping and hoping.\n\n - Strong autonomy, ownership, and technical leadership across teams, including mentoring senior engineers and driving architectural decisions.\n\n - Comfortable operating in a dynamic, fast-paced, outcome-driven environment.\n\n\nGREAT TO HAVE\n\n - Hands-on experience with post-training algorithms and infrastructure, including SFT and RL.\n\n - Experience with content understanding and/or information retrieval in structured-document-heavy domains.\n\n - Prior work on RAG systems involving data sources in different formats (Google Docs, PDFs, DOCX, etc.).\n\n\n\n\n\nBASE SALARY RANGES\n\n - Staff: $188,000 - $235,000\n\n - Senior Staff: $220,000 - $270,000\n\nThe base salary range represents the minimum and maximum of the salary range for this position based at our San Francisco headquarters. The actual base salary offered for this position will depend on numerous factors, including individual proficiency, anticipated performance, and the location of the selected ca","salary_min":220000,"salary_max":270000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["rag","llm","nlp","agents","search"],"apply_url":"https://jobs.ashbyhq.com/ironcladhq/4be2d35a-9aa0-415c-ba13-30da080158ad/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-05T15:18:25.532Z","expires_at":"2026-11-13T16:58:49.178687Z","created_at":"2026-08-26T13:41:23.863704Z","updated_at":"2026-10-14T16:58:49.181975Z","company_name":"Ironclad","company_slug":"ironclad","company_logo_url":"https://www.google.com/s2/favicons?domain=ironcladapp.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/42d0b3d6-fb09-4db9-9c43-b451572a2005"},{"id":"ca2fdd95-1e5c-4e50-a29f-43b1bfada0c0","company_id":"01b03876-5b01-47ab-82b9-696293e861b9","title":"Sr Offensive AI Security Engineer II","slug":"sr-offensive-ai-security-engineer-ii-81bf05f8","description":"Who we are \n Samsara (NYSE: IOT) is the pioneer of the Connected Operations™ Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. At Samsara, we are helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing — and we are excited to help digitally transform their operations at scale. \n Working at Samsara means you’ll help define the future of physical operations and be on a team that’s shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you’ll have the autonomy and support to make an impact as we build for the long term. \n About the role: \n Samsara is building a new role at the intersection of security engineering and applied AI. You'll be the most AI-forward technologist on our Application Security team. You'll use frontier AI models to find vulnerabilities across our cloud, software, and firmware before attackers do. You'll also evaluate each new model for security use and help build the guardrails for adopting AI safely. This role reports into our AppSec team and has a clear path to shape Samsara's AI security strategy.\n  \n This role is open to candidates residing in the US except the San Francisco Bay Metro Area, NYC Metro Area, and Washington, D.C. Metro Area.\n You should apply if: \n \n You want to impact the industries that run our world: Your efforts will result in real-world impact – helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.\n You are the architect of your own career: If you put in the work, this role won’t be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment and master your craft in a hyper growth environment.\n You’re energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.\n You want to be with the best: At Samsara, we win together, celebrate together and support each other. You will be surrounded by a high-calibre team that will encourage you to do your best. \n \n In this role, you will: \n \n Deliver accurate, consistent security assessments of Samsara's software, code,  and firmware, evaluating their resilience against AI-driven/adversarial attacks, and implementing fixes. \n Create and translate  general AI security findings into Samsara-specific fixes for our application security layer— including adapting techniques built for software to apply to hardware and embedded firmware.\n Find both AI-specific and traditional vulnerabilities (e.g., injection, IDOR) using AI-driven offensive validation.\n Build and pressure-test novel AI-enabled security tooling and workflows, then drive adoption of what proves valuable across the broader security org.\n Benchmark each new frontier model against the previous one for security tasks, and build the release criteria and guardrails for adopting it.\n Support AppSec vulnerability triage through an office-hours rotation, and help run our bug bounty program, with potential expansion to hardware.\n Partner cross-functionally with engineering, hardware, and leadership.  Present findings to security and executive leadership  as Samsara's go-to expert on AI-related security risk.\n Help define \u0026 drive  Samsara's medium- and long-term strategy for adopting AI safely and effectively within security operations.\n Use frontier AI/LLM tools (e.g., Anthropic's and OpenAI's cybersecurity-specific programs) hands-on for both offensive validation (red-teaming our own systems) and defensive tooling — required experience integrating, securing, or red-teaming AI-enabled systems (e.g., LLMs, agentic workflows, prompt injection/adversarial testing) is essential to this role.\n Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices\n \n Minimum requirements for the role: \n \n 8+ years of experience in application security, vulnerability management, threat modeling, offensive security, or a closely related discipline, with demonstrated application fix impact and influence across a large part of an organization.\n Hands-on experience building AI or agentic workflows for security use cases.\n Experience conducting technical security assessments (e.g. offensive security o","salary_min":157675,"salary_max":238500,"location":"Denver, CO","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","cloud","rag","security","llm"],"apply_url":"https://www.samsara.com/company/careers/roles/8247432?gh_jid=8247432","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T18:27:37Z","expires_at":"2026-11-13T16:55:44.416692Z","created_at":"2026-10-14T15:19:50.782833Z","updated_at":"2026-10-14T16:55:44.420231Z","company_name":"Samsara","company_slug":"samsara","company_logo_url":"https://www.google.com/s2/favicons?domain=www.samsara.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ca2fdd95-1e5c-4e50-a29f-43b1bfada0c0"},{"id":"321d040b-e93c-4893-b3a9-be805e5542af","company_id":"01b03876-5b01-47ab-82b9-696293e861b9","title":"Sr Offensive AI Security Engineer II","slug":"sr-offensive-ai-security-engineer-ii-b94b4b01","description":"Who we are \n Samsara (NYSE: IOT) is the pioneer of the Connected Operations™ Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. At Samsara, we are helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing — and we are excited to help digitally transform their operations at scale. \n Working at Samsara means you’ll help define the future of physical operations and be on a team that’s shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you’ll have the autonomy and support to make an impact as we build for the long term. \n About the role: \n Samsara is building a new role at the intersection of security engineering and applied AI. You'll be the most AI-forward technologist on our Application Security team. You'll use frontier AI models to find vulnerabilities across our cloud, software, and firmware before attackers do. You'll also evaluate each new model for security use and help build the guardrails for adopting AI safely. This role reports into our AppSec team and has a clear path to shape Samsara's AI security strategy.\n  \n This role is open to candidates residing in the US except the San Francisco Bay Metro Area, NYC Metro Area, and Washington, D.C. Metro Area.\n You should apply if: \n \n You want to impact the industries that run our world: Your efforts will result in real-world impact – helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.\n You are the architect of your own career: If you put in the work, this role won’t be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment and master your craft in a hyper growth environment.\n You’re energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.\n You want to be with the best: At Samsara, we win together, celebrate together and support each other. You will be surrounded by a high-calibre team that will encourage you to do your best. \n \n In this role, you will: \n \n Deliver accurate, consistent security assessments of Samsara's software, code,  and firmware, evaluating their resilience against AI-driven/adversarial attacks, and implementing fixes. \n Create and translate  general AI security findings into Samsara-specific fixes for our application security layer— including adapting techniques built for software to apply to hardware and embedded firmware.\n Find both AI-specific and traditional vulnerabilities (e.g., injection, IDOR) using AI-driven offensive validation.\n Build and pressure-test novel AI-enabled security tooling and workflows, then drive adoption of what proves valuable across the broader security org.\n Benchmark each new frontier model against the previous one for security tasks, and build the release criteria and guardrails for adopting it.\n Support AppSec vulnerability triage through an office-hours rotation, and help run our bug bounty program, with potential expansion to hardware.\n Partner cross-functionally with engineering, hardware, and leadership.  Present findings to security and executive leadership  as Samsara's go-to expert on AI-related security risk.\n Help define \u0026 drive  Samsara's medium- and long-term strategy for adopting AI safely and effectively within security operations.\n Use frontier AI/LLM tools (e.g., Anthropic's and OpenAI's cybersecurity-specific programs) hands-on for both offensive validation (red-teaming our own systems) and defensive tooling — required experience integrating, securing, or red-teaming AI-enabled systems (e.g., LLMs, agentic workflows, prompt injection/adversarial testing) is essential to this role.\n Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices\n \n Minimum requirements for the role: \n \n 8+ years of experience in application security, vulnerability management, threat modeling, offensive security, or a closely related discipline, with demonstrated application fix impact and influence across a large part of an organization.\n Hands-on experience building AI or agentic workflows for security use cases.\n Experience conducting technical security assessments (e.g. offensive security o","salary_min":157675,"salary_max":238500,"location":"Seattle, WA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["rag","cloud","security","llm","agents"],"apply_url":"https://www.samsara.com/company/careers/roles/8247424?gh_jid=8247424","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T18:25:48Z","expires_at":"2026-11-13T16:55:44.431872Z","created_at":"2026-10-14T15:19:50.770121Z","updated_at":"2026-10-14T16:55:44.435776Z","company_name":"Samsara","company_slug":"samsara","company_logo_url":"https://www.google.com/s2/favicons?domain=www.samsara.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/321d040b-e93c-4893-b3a9-be805e5542af"},{"id":"5511badc-9403-413b-a158-ba1643b2f72a","company_id":"7f631da3-2c29-4575-a413-a9ed92f8af53","title":"Principal AI Architect","slug":"principal-ai-architect-5df377a8","description":"About the role:\n At Rubrik IT, we are transforming how the enterprise thinks and operates — building a \"thinking enterprise\" where intelligent, agentic systems reason across our data, tools, and workflows to drive speed, quality, and measurable business impact. As an AI Architect, you will be a pivotal technical leader driving AI Native solutions that make this vision real.\n You will operate where AI innovation, top-tier engineering, and business outcomes converge, acting as the technical bridge between complex business problems and cutting-edge AI execution. Leveraging our robust Enterprise AI Platform, you will translate ambiguous problems into concrete AI solution designs and ensure their successful deployment and measurable impact.\n This role is designed for systems-thinkers and orchestrators with an AI-first mindset — leaders who move fluidly between hands-on building  AI  solutions, and who know how to harness model APIs, data sources, and workflow layers to build practical technology that serves the business.\n What you'll do:\n \n Lead applied AI solution design and architecture, breaking down ambiguous business problems into concrete, actionable AI solution designs.\n Contribute to the detailed design of large-scale, distributed AI/ML systems, ensuring performance, reliability, and security.\n Design and improve retrieval, prompting, tool-calling, and orchestration patterns for internal use cases such as knowledge assistants, workflow automation, and decision support.\n Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI applications across teams.\n Drive the hands-on development and implementation of key AI components, supporting both traditional and Generative AI model development and deployment.\n Build and deploy AI-enabled internal workflows that connect enterprise systems, data sources, model APIs, and automation layers.\n Leverage AI-assisted development to accelerate implementation, bringing a strong 'editor' mindset to ruthlessly audit, review, and secure AI-generated code against our standards for reliability, security, observability, and documentation.\n Lead the implementation and continuous improvement of MLOps pipelines, including automated model training, versioning, deployment, and monitoring.\n Develop and maintain evaluation approaches for output quality, retrieval accuracy, latency, and failure modes, and use findings to improve system performance over time.\n Help identify where human review, controls, and escalation paths are required to support responsible deployment of AI-enabled systems.\n Apply sound engineering judgment to balance speed, usability, risk, and maintainability in production and near-production environments.\n Proactively collaborate with executive leadership, data science, engineering, and product stakeholders to translate business use-cases into scalable AI solutions.\n Lead rigorous problem formulation by partnering with R\u0026D,, Security, Data, HR, Finance, and business stakeholders — ensuring we apply AI to the right problems before translating needs into scalable technical solutions.\n Provide technical leadership and mentorship to other AI/ML engineers, fostering a culture of engineering excellence and hands-on experimentation.\n Contribute reusable components, playbooks, and patterns that reduce duplicate effort and improve speed across the engineering environment.\n Technical architecture and AI implementations directly to measurable business outcomes, recognizing that technology serves the business.\n Actively research and evaluate cutting-edge AI/ML techniques, algorithms, and models to identify opportunities for platform enhancement and new solution development.\n \n Experience you'll need:\n \n 10+ years of core Software/Systems Engineering architecture experience, including 4+ years dedicated to building and deploying applied AI/ML systems in production.\n Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s/Ph.D. is a plus).\n Direct hands-on experience with Generative AI technologies, including LLMs, Retrieval-Augmented Generation (RAG), context graphs, memory management, and agentic AI architectures.\n Strong programming skills in Python, Java, or Go. Deep proficiency with GenAI orchestration frameworks (e.g., LangGraph, LlamaIndex, AutoGen, Semantic Kernel) and a working knowledge of traditional ML frameworks (e.g., PyTorch, TensorFlow).\n Extensive hands-on experience with the modern AI data ecosystem, including Vector Databases (e.g., Pinecone, Weaviate) and Graph Databases (e.g., Neo4j).\n Experience with distributed systems architecture, event-driven streaming platforms, and cloud AI/ML platforms (e.g., GCP Vertex AI, AWS SageMaker, Azure OpenAI).\n Strong ability to work across data, model, application, and infrastructure layers, operating effectively with incomplete information and ambiguous problems.\n Experience integrating applications, data sources, or internal platfor","salary_min":254300,"salary_max":356300,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["distributed-systems","embeddings","tensorflow","agents","mlops","generative-ai","llm","rag"],"apply_url":"https://www.rubrik.com/company/careers/departments/job.8223656?gh_jid=8223656","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T17:55:54Z","expires_at":"2026-11-13T17:03:40.178154Z","created_at":"2026-10-14T15:25:42.349947Z","updated_at":"2026-10-14T17:03:40.181542Z","company_name":"Rubrik","company_slug":"rubrik","company_logo_url":"https://www.google.com/s2/favicons?domain=rubrik.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5511badc-9403-413b-a158-ba1643b2f72a"},{"id":"4fadc663-d897-4c6c-9871-9df20f6050b2","company_id":"92ecd7f4-44ad-4dc4-8fe8-0c4cab4fa5db","title":"Senior Manager, Engineering","slug":"senior-manager-engineering-550cd2ce","description":"Jasper is the marketing agents platform, built to help enterprises orchestrate AI agents that execute marketing work at scale. Purpose-built for marketing teams, Jasper enables faster, more consistent execution across campaigns, personalization, localization, and compliance—while maintaining enterprise-grade control and governance.\n\nJasper is trusted by hundreds of enterprises worldwide, including Prudential, Cushman \u0026 Wakefield, and nearly 20% of the Fortune 500. Founded in 2021, Jasper has team members across the U.S., Australia, and France.\n\n\n\n\nABOUT THE ROLE\n\nAs a Senior Manager, Engineering on the IQ Platform team, you'll lead the vision and architecture behind the context layer that powers everything our AI agents do. Our agents run end-to-end marketing workflows with high quality and reliability because of the IQ layer. This is one of the most important technical areas of our product: the IQ Platform helps agents understand business context, deliver high-value intelligence, and execute complex marketing workflows.\n\n\n\nThis is a player-coach role: you'll lead a team of engineers while staying close to the architecture and code, acting as both a people leader and a hands-on technical contributor. You'll partner with Staff-level engineers to cut through ambiguity and drive technical strategy forward. Your guidance will shape the roadmap for our knowledge management and RAG systems, and drive the evolution of a context layer that learns and improves over time.\n\n\n\nThis fully remote role reports to the Senior Director, Engineering and is open to candidates located anywhere in the US.\n\n\n\n\n WHAT YOU WILL DO AT JASPER\n\n - Lead the IQ Platform team—set technical direction, coach engineers, and own delivery of major platform initiatives end-to-end\n\n - Chart and communicate the vision for our context layer, aligning engineering investments with business impact and product strategy\n\n - Build and scale our knowledge management system, including external integrations and RAG pipelines that feed high-quality context to our agents\n\n - Design and implement the architecture that makes our context layer self-improving—learning from usage, feedback, and new data to continuously raise quality\n\n - Act as a player-coach: write and review code alongside the team while mentoring engineers and raising the bar on technical excellence\n\n - Partner with Staff-level and senior engineers across the org as a strategic thinking partner on complex, ambiguous problems\n\n - Balance velocity and quality in a startup environment—shipping major systems pragmatically while keeping the architecture extensible\n\n - Collaborate cross-functionally with Product, Design, and other Engineering teams to ensure the IQ Platform powers real-world agent workflows at scale\n\n\n\n\nWHAT YOU WILL BRING TO JASPER \n\n - AI fluency—a working understanding of core AI concepts (LLMs, agents, copilots, tokens, credits, context windows, RAG, data governance, etc.), applied in the context of building and leading an AI platform engineering team\n\n - 10+ years of hands-on software engineering experience, with demonstrated people leadership and technical architecture ownership\n\n - Proven leadership at a company focused on business impact—you develop engineers, build healthy teams, and connect technical work to business outcomes\n\n - Flexibility to ship major systems in a startup environment—scrappy, velocity-oriented, and comfortable with ambiguity and evolving requirements\n\n - Ability to work effectively with Staff-level engineers as a true thinking partner on hard architectural and systems-design problems\n\n - Strong systems design and architecture skills, with experience building production platforms or infrastructure at scale\n\n - Nice to have: Hands-on experience with LLMs and applied LLM systems\n\n - Nice to have: RAG expertise, including retrieval pipelines, embedding strategies, and evaluation\n\n - Nice to have: Familiarity with LLM frameworks such as LangChain\n   \n   \n\n\nCOMPENSATION RANGE \n\nAt Jasper, we believe in pay transparency and are committed to providing our employees and candidates with access to information about our compensation practices. The expected base salary range offered for this role is $214,200 - $259,000. Compensation may vary based on relevant experience, skills, competencies, and certifications.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n - Comprehensive Health, Dental, and Vision coverage beginning on the first day for employees and their families\n\n - 401(k) program with up to 2% company matching\n\n - Equity grant participation\n\n - Flexible PTO with a FlexExperience budget ($900 annually) to help you make the most of your time away from work\n\n - FlexWellness program ($1,800 annually) to help support your personal health goals\n\n - Generous budget for home office set up \n\n - $1,500 annual learning and development stipend \n\n - 16 weeks of paid parental leave\n   \n   \n\nOur goal is to be a diverse workforce that is representative at all job levels as we know th","salary_min":214200,"salary_max":259000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["code-generation","agents","llm","rag"],"apply_url":"https://jobs.ashbyhq.com/Jasper%20AI/a6aa87ff-23ff-41ca-afc9-8a99141d9e2f/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T21:56:43.908Z","expires_at":"2026-11-13T16:55:27.151507Z","created_at":"2026-10-14T15:19:17.304097Z","updated_at":"2026-10-14T16:55:27.154383Z","company_name":"Jasper AI","company_slug":"jasper-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=jasper.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4fadc663-d897-4c6c-9871-9df20f6050b2"},{"id":"13d94c06-bee9-4b4a-974f-9db87d431a7c","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Director, Data Product Engineering","slug":"director-data-product-engineering-49454acb","description":"ABOUT THE ROLE \n  \n Natera is seeking a product engineering leader to build and lead the team that designs, delivers, and operates domain data products and AI-enabled analytical solutions on NDP (Natera Data Platform). You will report to the Head of Data \u0026 AI and partner with platform, governance, and product functions to turn data needs into certified, production-grade data and analytics products. \n  \n Natera follows a data mesh architecture with headless data products: domain-owned assets, not tied to any BI layer, built for both human and AI consumption . A data product is ready when it is semantically correct and AI-ready, not just numerically accurate. Your mandate is to make that standard repeatable across every domain while transforming how the team works: AI-native, 3–5x more productive, and self-service for the business. \n  \n Please note that this is role focuses on product side of data engineering (not platform). This person needs to demonstrate their ability to \n  \n (a) Build a self-service analytics product experience for business users and \n (b) Create a catalog of AI ready gold-standard cross functional data products \n (c) Create an operating model focusing on reusability, speed. and business value of data \n  \n that scale beyond one business domain. \n  \n Critical Priorities for This Role \n \n Standardize and scale how data products are built — one publishing standard, golden paths, and a common operating model across every domain. \n Make the team AI-native — agentic SDLC and AI-assisted development become how everyone works resulting in higher productivity and growth opportunities \n Deliver a 3–5x productivity gain — instrumented with baselines and delivery KPIs, not anecdotes. \n Raise data product quality — semantically correct, AI-ready, observable, ship-ready data products. \n Make analytics self-service — certified, discoverable products that business users and AI systems consume without an engineering queue. \n \n  \n RESPONSIBILITIES \n 1. Own Data Product Delivery \n \n Lead the build of cross functional data products, analytics experiences, and Golden KPI's that are essential to making data driven decisions across the business \n Create the operating model to deliver analytics to business users by leveraging embedded data/AI engineers with each business domain. \n Track and report delivery KPIs: time-to-delivery, certified dataset count, adoption by consuming teams, open production incidents. \n \n 2. Standardize \u0026 Scale How Data Products Are Built for Analytics and AI use \n \n Define and enforce the publishing standard for analytics products: semantically correct, AI-ready, lineage documented, ownership assigned, catalog entry complete. \n Establish the headless data product standard: domain-owned assets any authorized consumer — dashboard, workflow, or AI system — can use. \n Build golden paths (templates, examples, documented patterns) so every product starts from a known-good baseline, and make this the operating model for intake, build, certification, and support. \n \n 3. Build an AI-Native Engineering Competency \n \n Drive agentic data engineering as the default: agentic SDLC, AI pipeline generation, agents writing and validating dbt models, AI-driven testing, LLM tools in code review and documentation. \n Train every engineer to work AI-natively and redefine the working model — SDLC steps, roles, human-in-the-loop checkpoints, definition of done. \n Own a measurable plan to lift productivity 3–5x: baseline throughput and cycle time, instrument each change, report outcomes. \n \n 4. Raise the Data Product Quality Bar \n \n Enforce engineering standards on every production solution: CI/CD for pipelines, infrastructure as code, data observability, data quality frameworks. \n Implement agentic data quality: automated drift detection, root-cause identification, human-in-the-loop recovery. \n Meet HIPAA, RAQA, and data classification requirements at design time. If a product does not meet the bar, it does not ship. \n \n 5. Make Data Self-Service for Humans and AI \n \n Own the NDP contribution and discovery model: what gets published, how it is documented, and how human and AI consumers find and evaluate certified assets. \n Ensure every catalog entry carries semantic metadata and AI-readiness classification so business users and AI agents answer their own questions without an engineering ticket. \n Partner with the Data Governance Lead to embed data contracts, certification review, and access policy into the lifecycle so self-service is safe and correctly scoped. \n \n 6. Lead the Team and Set the Technical Bar \n \n Hire, coach, and grow data and analytics engineers; set clear expectations, give direct feedback, build real career paths. \n Stay hands-on: review architecture and code, debug production failures, and set the standard by example across Snowflake, AWS, Claude, Sigma, dbt, Fivetran, Python, and Airflow. \n Be the engineering face of data products to business stakeholders: transl","salary_min":186700,"salary_max":233400,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["llm","healthcare","rag","embeddings","agents","data-pipeline","code-generation"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6205809004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T21:32:23Z","expires_at":"2026-11-13T16:58:37.946383Z","created_at":"2026-10-14T15:22:15.586183Z","updated_at":"2026-10-14T16:58:37.94917Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/13d94c06-bee9-4b4a-974f-9db87d431a7c"},{"id":"0834e635-fd99-4ca3-8178-6ebe5211c479","company_id":"a0000000-0000-0000-0000-000000000003","title":"Software Engineer, Public Sector - New Grad","slug":"software-engineer-public-sector-new-grad-f9985367","description":"At Scale, our mission is to develop reliable AI systems for the world's most important decisions. For 10 years, Scale has provided the high-quality data and full-stack technologies that power the world's leading models, and has helped enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst \u0026 Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force.\n Public Sector engineers build the core product including the systems required to ingest and process federal datasets that support real-time decision-making in contested environments. As a New Grad Software Engineer on this team, you will own meaningful, mission-facing work from day one: shipping features, sitting with the government stakeholders who use them, and iterating fast.\n Example Projects\n \n Build multi-layered guardrails that keep agents safe and predictable in high-stakes federal environments\n Optimize data retrieval for agents, including RAG pipelines over large, heterogeneous federal datasets\n Build orchestration for fleets of asynchronous agents running long-horizon tasks\n Develop systems that automatically alert users to deviations and anomalies in incoming data\n Create interfaces that illustrate how an agent reached a decision, so operators can audit and trust its output\n Develop data pipelines and ML infrastructure that make previously siloed government data sources accessible to agents\n Build evaluation infrastructure that measures model reliability against mission requirements\n Ship full-stack tooling that lets analysts query, visualize, and explore mission data\n Deploy and harden applications into secure, air-gapped, and cloud-native government environments\n \n Requirements\n \n A graduation date in Fall 2026 or Spring 2027 with a Bachelor's degree (or equivalent) in a relevant field (Computer Science, EECS, Computer Engineering, Statistics)\n Product engineering experience such as building web apps full-stack, integrating with relevant APIs and services, talking to users, figuring out 'what' to build and then iterating\n Previous Product/Software Engineering internship experience\n Track record of shipping high-quality products and features at scale\n Experience building systems that process large volumes of data\n Experience with Python, Typescript, React, and/or MongoDB\n Strong written and verbal communication — you will work directly with government stakeholders and must be able to explain technical concepts to non-technical audiences\n Comfort with ambiguity and evolving requirements, and a willingness to learn new domains quickly\n \n Nice to Have\n \n Hands-on experience with LLMs, evaluations, or agentic systems — whether from internships, research, or personal projects\n Familiarity with cloud platforms (AWS, Azure, GCP), containerization (Docker), and orchestration (Kubernetes)\n Exposure to ETL, data modeling, data warehousing, or data governance\n Prior experience with, or demonstrated interest in, national security, defense, or civil government technology\n Open source contributions or a portfolio of shipped side projects\n \n How We Work\n Our credos shape how we make decisions and work as a team:\n \n Earn Customer Love — trust is earned with every interaction, every delivery, and every problem we solve\n Team Flow — ideas, energy, and support move freely across teams; optimize for the whole\n Quality is Our Cheat Code — the systems that deliver quality consistently are a structural advantage\n Find the 20% — find the inputs that drive the outcomes and execute them completely\n Write the Market — our research puts us at the frontier, and we bring our partners there first\n Three Moves Ahead — consider the consequences of consequences, and plan accordingly\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n The base salary range for this full-time position in the location of San Francisco is","salary_min":124000,"salary_max":162000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","rag","agents","llm"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4736426005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-23T17:55:35Z","expires_at":"2026-11-13T16:55:03.40806Z","created_at":"2026-10-14T15:18:53.026936Z","updated_at":"2026-10-14T16:55:03.411428Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0834e635-fd99-4ca3-8178-6ebe5211c479"},{"id":"d2cedd25-275e-45fc-abc7-cc0099a58267","company_id":"d8e15a46-b80d-4228-8e7b-34f00357f377","title":"Principal AI Engineer - Context - Agents and Context","slug":"principal-ai-engineer-context-agents-and-context-210c9d79","description":"Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.\n What is The Role \n The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses.\n As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic.\n The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack.\n What You Will Be Doing \n \n Own the production improvement loop for Context Engine: understand how extraction automations, retrieval tools and memory behave, based on offline evaluations and customer conversations and telemetry. You help find the failure modes, fix them, and prove the fix.\n Define how we iterate on agents and skills safely: versioning and rollout of prompts, skills and automations, regression coverage, staged and shadow evaluation, and the guardrails that let us change behaviour without breaking customers.\n Design the telemetry we need to make data-informed engineering decisions: what to capture from agent traces, tool calls and knowledge retrieval, how it lands in Elasticsearch, and how it feeds evaluation, dashboards and the feedback loop.\n Partner with the data science team on evaluation strategy: golden datasets, evaluators to gate on quality, latency and cost.\n Raise the bar across the team: review designs and PRs, mentor engineers in eval-driven development, and write the technical proposals that shape the roadmap.\n \n What You Bring\n \n 10+ years of software engineering experience, with the recent years spent shipping and operating AI-driven products on real production traffic, ideally products with public APIs and data models that had to evolve without breaking customers.\n A track record of eval-driven product improvement: you have diagnosed agent or LLM behaviour from traces and user feedback, designed the evaluation that exposed the problem, shipped the fix and measured the outcome.\n Direct experience building agents with state and memory, and iterating on prompts, skills and tool behaviour safely in production.\n Familiarity with MCP, including exposing public MCP servers and tools.\n Experience designing telemetry for AI systems, and using it to make engineering and product decisions.\n Experience running product experiments end to end: instrumentation, unattended execution, and interpreting results.\n Experience building products with public APIs and evolving data models, and the judgement that comes with maintaining external contracts as a product changes.\n Strong backend engineering skills in either Python or TypeScript: APIs, stateful workflows, data pipelines and production services.\n Comfort working with data scientists, engineers, and product managers as peers, translating between measurement and shipping, and communicating trade-offs clearly.\n A pragmatic, low-ego style suited to a distributed, async-first team.\n \n Bonus Points\n \n Experience with agent frameworks such as LangGraph, CrewAI, Claude Agent SDK, or similar.\n Agentic retrieval experience, including knowledge representation.\n Practical Elasticsearch experience.\n Compensation for this role is in the form of base salary.  This role does not have a variable compensation component.  The typical starting salary range for new hires in this role is listed below. \n These ranges represent the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting.  We may ultimately ","salary_min":154000,"salary_max":243600,"location":"Canada","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["search","llm","data-pipeline","rag","agents"],"apply_url":"https://jobs.elastic.co/jobs?gh_jid=8223688\u0026gh_jid=8223688","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-22T16:57:27Z","expires_at":"2026-11-13T16:57:57.610115Z","created_at":"2026-10-14T15:21:37.657912Z","updated_at":"2026-10-14T16:57:57.613131Z","company_name":"Elastic","company_slug":"elastic","company_logo_url":"https://www.google.com/s2/favicons?domain=www.elastic.co\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d2cedd25-275e-45fc-abc7-cc0099a58267"},{"id":"4b2099f7-65eb-4d43-913b-fb05c645e26e","company_id":"46e2df02-755e-46ce-a584-3f5881f34183","title":"Forward Deployed AI Engineer","slug":"forward-deployed-ai-engineer-3a24acc2","description":"About Turing \n Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com .  \n  \n Role: Forward-Deployed AI Engineer — Enterprise AI Location: New York City Experience Level: 8+ years Compensation: $200,000–$250,000 base salary + equity Work Model: In-office, typically three to five days per week, depending on client requirements\n Why Join Enterprise AI?\n Enterprise AI is a distinct business unit within Turing, operating with the speed, focus, and ownership of a rapidly scaling startup. When it comes to applied AI in the workplace, you’ll define the frontier by building beyond it.\n You’ll join the initial team of Forward-Deployed Engineers, shaping how we work while solving enterprise-grade problems for the world’s leading enterprises. As a Forward-Deployed AI Engineer, you’ll own outcomes end to end—from identifying high-value customer problems and shaping architecture to building production systems and working directly with senior client leaders.\n You’ll develop generative AI solutions in the real world, driving measurable impact and return on investment within large institutions. Enterprise AI is intense, intellectually challenging work. It won’t be for everyone, but it may be exactly the challenge you’re looking for. \n As a Forward-Deployed Engineer, You Will\n Find the Problem Worth Solving\n Work directly with customers to navigate ambiguity, uncover high-value opportunities, challenge assumptions, and translate complex business needs into clear technical direction. This requires curiosity and business acumen as you discover business workflows and jointly define the outcomes an AI solution should drive.\n Build Beyond the Frontier\n Architect and build production AI systems, from multi-agent workflows, orchestration, tool use, context engineering, memory and state, and retrieval-augmented generation (RAG) to the APIs, data pipelines, integrations, and distributed systems that make them reliable at scale.\n Own the Outcome\n You won’t simply be given a list of responsibilities—you’ll have the authority to own the outcome for your client and make a real-world impact through your work. You’ll lead solutions from discovery through deployment and measurable impact while staying hands-on and owning evaluation, observability, security, reliability, latency, scalability, and cost.\n The Anti-Sell\n High ownership and intensity: This role requires exceptional ownership and accountability. You’ll work with clients who have uncompromising expectations for quality, making this a demanding environment—and an ideal proving ground for people who seek difficult, high-impact challenges.\n Client-first and in-office: Our work is client-first, which means in-person collaboration may be required either at the client site or at our planned Manhattan office near Madison Square Park. Depending on your assignment and client needs, you should expect to work in person three to five days per week and be based in the New York City area or within a reasonable commuting distance.\n You’ll help write the playbook: Enterprise AI operates like a startup. You may not always have an established process or playbook because you’ll be expected to apply your expertise, judgment, and technical depth to create one. \n Our Forward-Deployed Engineers Typically Have\n Deep production engineering experience: Typically 8+ years building and operating sophisticated software systems, with expertise in Python, backend systems, APIs, databases, distributed systems, system design, and cloud infrastructure.\n Hands-on agentic AI experience: Experience building production LLM and agent systems using orchestration, tool use, context engineering, memory and state, retrieval, and multistep workflows.\n End-to-end systems ownership: A track record of taking complex systems from architecture through deployment and scale, with a practical understanding of evaluation, observability, reliability, security, guardrails, latency, scalability, and cost.\n Exceptional customer and technical leadership: The ability to enter unfamiliar environments, earn trust quickly, navigate ambiguity, challenge assumptions","salary_min":200000,"salary_max":250000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","healthcare","llm","data-pipeline","rag","generative-ai","distributed-systems","agents"],"apply_url":"https://job-boards.greenhouse.io/turing/jobs/6192730004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-17T22:40:50Z","expires_at":"2026-11-13T16:55:55.932569Z","created_at":"2026-10-14T15:20:05.401908Z","updated_at":"2026-10-14T16:55:55.93534Z","company_name":"Turing","company_slug":"turing","company_logo_url":"https://www.google.com/s2/favicons?domain=turing.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4b2099f7-65eb-4d43-913b-fb05c645e26e"},{"id":"e7c1ea9a-f952-4232-a083-b336ad7cc931","company_id":"e455f75a-a424-4955-9844-afebe8ea6eb4","title":"Principal Machine Learning Engineer, SecureAI","slug":"principal-machine-learning-engineer-secureai-0a297b19","description":"Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.\n About Okta Secures AI Team \n  The Okta for AI Agents Team is building the future of digital identity management. The way companies are using agents and allowing them access is undergoing a fundamental shift, enabling teams to move fast while staying secure. \n Our North Star is clear: To get AI right, you have to get identity right. We are not just managing identities; we are building the industry's first Identity Security Fabric for the agentic era. As organizations rapidly deploy AI, these non-human entities are acting with access to critical tools, often bypassing traditional perimeters and creating a 'Shadow AI' crisis. Our mission is to move identity from a reactive gatekeeper to a unified control plane, transforming AI risk into business ROI.\n We are tackling this by implementing a comprehensive four-pillar maturity model: Discover, Onboard, Protect, and Govern. We are driving innovation by defining the standards for Agentic Identity—including pioneer work with securing AI Agents and support for protocols like MCP. You will be helping us build the infrastructure that allows enterprises to scale AI safely, ensuring every access decision—whether made by a human or an automated agent—is authenticated, authorized, and audited at scale.\n We are a diverse team of engineers, product managers, and designers who are bringing Okta’s expertise in identity to define the future of Agent Identity management, focusing on enablement while staying secure.\n About the role \n In this role, you will join the Agent Access Policies sub-team under Okta Secures AI as a Principal Machine Learning (ML) Engineer to advance Okta's authorization capabilities. By replacing static, rule-based systems with dynamic AI security mechanisms, you will deliver real-time threat inspection, agent intent evaluation, and behavioral analysis—ensuring autonomous AI agents operate safely within specified bounds.\n Your core mission will be architecting and driving features for our unified control plane to establish the premier Identity Security Fabric for the agentic era. In doing so, you will help execute our guiding principle: \"To get AI right, you have to get identity right.\"\n What you’ll be doing  \n \n Implement intent-based enforcement to verify agent runtime requests match their intended purpose, requiring key capabilities.\n Apply LLM reasoning and prompt parsing to interpret prompts, tool payloads, and intent in real time.\n Integrate low-latency inference or semantic evaluation engines directly into the API gateway request path.\n Use embeddings, vector search, or zero-shot classification to score alignment between agent intent and executed actions.\n Design confidence-scored decision engines that feed semantic verification results into policy frameworks (e.g., Cedar).\n Establish evaluation benchmarks, prompt injection defenses, and guardrails to prevent bypasses or false positives.\n Architect scalable ML and Generative AI systems integrating retrieval, inference, and evaluation pipelines.\n Optimize prompting, context retrieval, and RAG workflows for accuracy, safety, and efficiency in Claude-based systems.\n Build automated evaluation pipelines to measure model quality, correctness, groundedness, and safety in production.\n Implement schema validation, structured output enforcement, and guardrails to ensure reliable, compliant AI outputs.\n Mentor and coach engineers to support team and community growth.\n \n What you’ll bring to the role \n \n 10+ years of software development experience, with strong programming expertise in Python (and familiarity with Go or Typescript a plus).\n Hands-on experience with applied machine learning, from feature engineering to training and fine-tuning models.\n Hands-on experience with modern Generative AI platforms (AWS Bedrock, OpenAI, Anthropic, etc.).\n Deep understanding of retrieval-augmented generation (RAG), embeddings, and knowledge-base workflows.\n Hands-on experience with LiteLLM, LangGraph, LangChain, LlamaIndex, MCP, or other related AI agent frameworks.\n Familiarity with ML frameworks (FastAPI, PyTorch, TensorFlow, Spark ML) and workflow orchestration tools (Airflow, etc.).\n Experience defining evaluation metrics, pipelines, and feedback loops for ML/GenAI systems.\n Proven ability to collaborate with product and engineering teams to drive greenfield initiatives forward, navigate unknowns, and iterate quickly and frequently.\n Experience building tools or infrastructure for AI/ML ap","salary_min":192000,"salary_max":264000,"location":"Toronto, Canada","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["pytorch","rag","fine-tuning","cloud","llm","tensorflow","generative-ai","agents"],"apply_url":"https://www.okta.com/company/careers/opportunity/8208377?gh_jid=8208377","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-16T22:30:47Z","expires_at":"2026-11-13T16:58:07.259813Z","created_at":"2026-10-14T15:21:47.264548Z","updated_at":"2026-10-14T16:58:07.262575Z","company_name":"Okta","company_slug":"okta","company_logo_url":"https://www.google.com/s2/favicons?domain=okta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e7c1ea9a-f952-4232-a083-b336ad7cc931"},{"id":"9b730dc7-f409-4bcd-9f2a-dd0979c6cdab","company_id":"280644c0-35ed-4161-9de3-24c4751b2c00","title":"Senior AI Software Engineer, Risk - Insurance Claims Management","slug":"senior-ai-software-engineer-risk-insurance-claims-management-e42d796a","description":"At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.\n The Risk Tech engineering organization is committed to tangibly reducing accident frequency, saving lives, and managing costs to enhance the safety and affordability of rides.\n Claim Management is a core financial function for Lyft. Each claim touches complex workflows, multiple stakeholders, sensitive data, financial reserves, regulatory processes, and significant financial liabilities.\n This role offers the opportunity to define a leading claims management system for the industry. Our vision is to establish a single, Unified Risk Platform where comprehensive claims workflows across all business lines are efficiently administered, communications are consolidated, and data is structured to facilitate data-driven insights and decisions.\n This enables cost-efficient claims operations, mitigates risks and expenses as Lyft scales, and ensures people receive assistance proactively and accurately.\n We are seeking a Senior Software Engineer to contribute to the technical direction, drive architectural decisions, and lead the development of a highly reliable, scalable, and intelligent Risk Management Information System that powers our insurance platform. You will collaborate with passionate colleagues from Engineering, Data Science, Product and Claim Operations to deliver end-to-end solutions.\n  Responsibilities: \n \n Define and drive the long-term technical roadmap for claims management systems, aligning priorities with Product, Claim Operations, Data Science, and other stakeholders.\n Provide architectural leadership, make informed design tradeoffs, and guide strategic decisions across multiple engineering teams.\n Understand claim handling workflows, identify bottlenecks and multiply their productivity by driving platform innovations and resolving pain points with AI automation solutions\n Deliver scalable, reliable, high quality and well-tested solutions and code.\n Collaborate closely with Lyft internal teams and external insurance partners to ensure technical alignment and scalable integrations.\n Lead code reviews, design reviews, production on-call support and incident triaging process.\n Invest in the engineering community through knowledge sharing via brown bags, tech talks, and suggesting improvements to team processes and engineering practices.\n \n Experience: \n \n B.S., M.S. in Computer Science or related technical field or relevant work experience.\n 6+ years of professional experience building scalable distributed systems with high availability, observability and reliability requirements.\n Proven ability to build end-to-end products and platforms, collaborating effectively with various stakeholders e.g. Backend, Frontend, Data Engineering, Data Science, Claim Operations, and Product teams.\n Strong ability to reason about tradeoffs in system design, architecture, domain modeling, and operational cost.\n Experience developing AI features using multimodal LLMs, RAG pipelines, agentic workflows, and other relevant AI frameworks.\n Experience driving AI adoption or AI automation tools to enhance development efficiency\n Experience improving performance with robust evals,agent tracing tools, fine-tuning LLMs, optimizing RAG pipelines and implementing feedback loops.\n Proficiency in Python. Knowledge of React/Next.js and data analysis is a plus.\n Experience with data processing frameworks and tools like Spark, Kafka, Airflow and cloud provisioning environments etc.\n Strong oral and written interpersonal skills.\n Preferred: \n \n Prior experience in insurance, claims management, workflow orchestration or external vendors is a strong plus.\n Has knowledge about AI safety and alignment considerations.\n Background in machine learning, NLP, or related fields.\n Experience with A/B testing and experimentation frameworks\n \n \n Benefits: \n \n Great medical, dental, and vision insurance options with additional programs available when enrolled\n Mental health benefits\n Family building benefits\n Child care and pet benefits\n 401(k) plan to help save for your future\n In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off\n 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible\n Subsidized commuter benefits\n Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program\n \n Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent","salary_min":136160,"salary_max":170200,"location":"Seattle, WA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["rag","distributed-systems","agents","llm","fine-tuning","alignment","nlp"],"apply_url":"https://app.careerpuck.com/job-board/lyft/job/8806969002?gh_jid=8806969002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-14T21:09:50Z","expires_at":"2026-11-13T16:57:53.456022Z","created_at":"2026-10-14T15:21:33.513659Z","updated_at":"2026-10-14T16:57:53.459975Z","company_name":"Lyft","company_slug":"lyft","company_logo_url":"https://www.google.com/s2/favicons?domain=www.lyft.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9b730dc7-f409-4bcd-9f2a-dd0979c6cdab"}],"page":1,"per_page":20,"total":349,"total_is_exact":true,"total_pages":18}