{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n SR. SOFTWARE ENGINEER (AI DATA ENGINEERING) \n Be a member of the Artificial Intelligence Data Engineering Software team, building AI-powered tools and automation that solve real engineering problems for our launch vehicles and spacecraft.\n Our team builds software that takes recurring, time-consuming work off engineers' plates so they can focus on hard engineering, from automated review of flight and test data to AI agents that help engineers prepare for missions and investigate anomalies. Your work will directly accelerate rapid reuse of the Falcon launch vehicle, support the world's largest satellite communication and AI constellations, and contribute to the development of the world's largest rocket capable of sending humans to Mars.\n You will work side-by-side with engineers across avionics, propulsion, structures, and more to find where their time goes and build tools that give it back, using agentic AI systems, MCP servers, RAG-based search, data pipelines over large telemetry datasets, and traditional automation where it is the right answer. You will own problems end-to-end: scoping the solution with the engineers who have the problem, rolling it out, measuring the time it saves, and maintaining it as the vehicle and processes evolve.\n Aerospace experience is not strictly required - we are looking for smart, motivated, collaborative engineers who love solving problems and want to make an impact on an inspiring mission.\n RESPONSIBILITIES: \n \n Partner directly with engineering teams to identify their most time-consuming, repetitive, or error-prone work, then design and build tools that automate it\n Build automation that processes large volumes of engineering data, including telemetry, hardware design databases, configuration files, and manufacturing and test records, to automate data review, flag anomalies, and surface insights\n Design agentic AI systems, multi-agent workflows, and MCP servers that let engineers query, analyze, and act on data across many internal systems\n Integrate tools into CI/CD pipelines, mission workflows, and review/sign-off processes so automation runs where engineers already work\n Build internal web dashboards that give engineers a clear view of mission status, open work, and data review results\n Rapidly prototype and iterate, then take tools to a production-grade, maintainable state\n Thoroughly test and validate all tools, including evaluating AI outputs against known-good results, so engineers can trust them in a flight-critical environment\n Prioritize work by return on investment and track the engineering hours saved\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in computer science, data science, engineering, math, or physics; OR 4+ years of professional experience building automation for practical engineering use cases in lieu of a degree\n 5+ years of experience in software engineering with a focus on automation tools and/or AI integrations (professional and personal projects are applicable)\n 5+ years of programming experience in Python\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Track record of building tools that engineers or other end users actually adopted, including gathering requirements directly from users and iterating on their feedback\n Comfort processing and analyzing large engineering or scientific datasets (time-series telemetry, sensor data, logs, etc.) using tools such as pandas and NumPy\n Built code or configuration generators that turn a source of truth (design databases, spreadsheets, schemas) into validated outputs\n Hands-on work building complex agentic AI systems and multi-agent workflows, including prompt and context engineering, tool/function calling, and MCP servers or agent skills\n Ability to design evaluations for LLM-based tools, and the judgment to know when a deterministic script is a better fit than an AI model\n Skilled at integrating with many systems through REST APIs, and quickly learning unfamiliar internal tools and data sources\n Working knowledge of relational (PostgreSQL), non-relational, and vector databases, with strong SQL skills\n Full-stack experience building web applications or dashboards (e.g., React/TypeScript frontends with Python backends such as FastAPI or Flask)\n Proficiency developing on Linux, with a solid understanding of Git, testing, CI/CD, and monitoring\n Familiarity deploying containerized applications using Docker and Kubernetes\n Familiarity with hardware engineering concepts (electrical schematics and wiring, sensors, propulsion, controls, or test and manufacturing processes), or a background as a hardware or test engineer who moved into software\n Strong communication skills and ability to explain technical tradeoffs to engineers in other disc","salary_min":165000,"salary_max":265000,"location":"Hawthorne, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","api-design","agents","embeddings","data-pipeline","data-engineering"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8880858002?gh_jid=8880858002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T00:39:29Z","expires_at":"2026-11-13T17:02:51.84726Z","created_at":"2026-10-14T15:24:54.877845Z","updated_at":"2026-10-14T17:02:51.850729Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/103173eb-39e0-49ad-90d3-4aced5c995f3"},{"id":"a8096508-4339-4acb-af22-bfdb3dfc7cd2","company_id":"01048ffd-9864-41e0-a719-14b849fbcbcd","title":"Software Engineer (AI Data Engineering)","slug":"software-engineer-ai-data-engineering-93f6b849","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n SOFTWARE ENGINEER (AI DATA ENGINEERING) \n Be a member of the Artificial Intelligence Data Engineering Software team, building AI-powered tools and automation that solve real engineering problems for our launch vehicles and spacecraft.\n Our team builds software that takes recurring, time-consuming work off engineers' plates so they can focus on hard engineering, from automated review of flight and test data to AI agents that help engineers prepare for missions and investigate anomalies. Your work will directly accelerate rapid reuse of the Falcon launch vehicle, support the world's largest satellite communication and AI constellations, and contribute to the development of the world's largest rocket capable of sending humans to Mars.\n You will work side-by-side with engineers across avionics, propulsion, structures, and more to find where their time goes and build tools that give it back, using agentic AI systems, MCP servers, RAG-based search, data pipelines over large telemetry datasets, and traditional automation where it is the right answer. You will own problems end-to-end: scoping the solution with the engineers who have the problem, rolling it out, measuring the time it saves, and maintaining it as the vehicle and processes evolve.\n Aerospace experience is not strictly required - we are looking for smart, motivated, collaborative engineers who love solving problems and want to make an impact on an inspiring mission.\n RESPONSIBILITIES: \n \n Partner directly with engineering teams to identify their most time-consuming, repetitive, or error-prone work, then design and build tools that automate it\n Build automation that processes large volumes of engineering data, including telemetry, hardware design databases, configuration files, and manufacturing and test records, to automate data review, flag anomalies, and surface insights\n Design agentic AI systems, multi-agent workflows, and MCP servers that let engineers query, analyze, and act on data across many internal systems\n Integrate tools into CI/CD pipelines, mission workflows, and review/sign-off processes so automation runs where engineers already work\n Build internal web dashboards that give engineers a clear view of mission status, open work, and data review results\n Rapidly prototype and iterate, then take tools to a production-grade, maintainable state\n Thoroughly test and validate all tools, including evaluating AI outputs against known-good results, so engineers can trust them in a flight-critical environment\n Prioritize work by return on investment and track the engineering hours saved\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in computer science, data science, engineering, math, or physics; OR 4+ years of professional experience building automation for practical engineering use cases in lieu of a degree\n 1+ years of experience in software engineering with a focus on automation tools and/or AI integrations (professional and personal projects are applicable)\n 1+ years of programming experience in Python\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Track record of building tools that engineers or other end users actually adopted, including gathering requirements directly from users and iterating on their feedback\n Comfort processing and analyzing large engineering or scientific datasets (time-series telemetry, sensor data, logs, etc.) using tools such as pandas and NumPy\n Built code or configuration generators that turn a source of truth (design databases, spreadsheets, schemas) into validated outputs\n Hands-on work building complex agentic AI systems and multi-agent workflows, including prompt and context engineering, tool/function calling, and MCP servers or agent skills\n Ability to design evaluations for LLM-based tools, and the judgment to know when a deterministic script is a better fit than an AI model\n Skilled at integrating with many systems through REST APIs, and quickly learning unfamiliar internal tools and data sources\n Working knowledge of relational (PostgreSQL), non-relational, and vector databases, with strong SQL skills\n Full-stack experience building web applications or dashboards (e.g., React/TypeScript frontends with Python backends such as FastAPI or Flask)\n Proficiency developing on Linux, with a solid understanding of Git, testing, CI/CD, and monitoring\n Familiarity deploying containerized applications using Docker and Kubernetes\n Familiarity with hardware engineering concepts (electrical schematics and wiring, sensors, propulsion, controls, or test and manufacturing processes), or a background as a hardware or test engineer who moved into software\n Strong communication skills and ability to explain technical tradeoffs to engineers in other discipli","salary_min":145000,"salary_max":195000,"location":"Hawthorne, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["data-pipeline","llm","api-design","embeddings","agents","data-engineering"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8880868002?gh_jid=8880868002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T00:39:23Z","expires_at":"2026-11-13T17:02:51.635632Z","created_at":"2026-10-14T15:24:54.677511Z","updated_at":"2026-10-14T17:02:51.639609Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a8096508-4339-4acb-af22-bfdb3dfc7cd2"},{"id":"64d23aa4-9ec2-400e-a028-2a5364b6e205","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Machine Learning Platform - Gen AI","slug":"software-engineer-machine-learning-platform-gen-ai-ba861ebf","description":"About the Team \n DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.\n About the Role \n You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll work across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability. This role is ideal for an engineer who enjoys pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly.\n You’re excited about this opportunity because you will… \n \n Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company.\n Work on our open-weights serving stack — real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.\n Design scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases\n Push the cost and latency frontier of GPU inference — turning batch jobs that took days into hours and cutting inference cost by multiples — while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in.\n Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence.\n Partner closely with ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives.\n Shape the future of DoorDash’s centralized GenAI platform — including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimization, and other post-training and agentic techniques — enabling the next generation of AI-powered products, agents, automation, and personalization.\n \n We’re excited about you because… \n \n B.S., M.S., or PhD. in Computer Science or equivalent\n 3+ years of industry experience in software engineering\n Strong backend engineering fundamentals, especially in Python and distributed systems.\n Experience building production services, APIs, data pipelines, or ML infrastructure at scale.\n Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization.\n Hands-on experience with LLM inference and/or fine-tuning of open-weight models in production — serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine-tuning (SFT/DPO/LoRA).\n Ability to work across ambiguous, fast-moving technical areas and turn customer use cases into reusable platform capabilities\n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n \n Nice To Haves \n \n Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM) in production\n Experience with distributed/multi-node fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation\n GPU performance work — multi-node/distributed inference, KV-cache/memory optimization, quantization (FP8/INT8/AWQ/GPTQ), or cold-start/throughput tuning\n Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal), or high-throughput batch systems\n Experience with LLM gateways, model routing, vendor abstraction, or cost attribution\n Experience building developer platforms, internal platforms, or self-serve infrastructure\n Experience building and deploying AI a","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["generative-ai","cloud","embeddings","mlops","fine-tuning","llm","distributed-systems","agents"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8264542","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:32Z","expires_at":"2026-11-13T17:03:28.762428Z","created_at":"2026-10-14T15:25:31.403168Z","updated_at":"2026-10-14T17:03:28.76533Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/64d23aa4-9ec2-400e-a028-2a5364b6e205"},{"id":"a54be057-d871-4092-a1aa-9f775c325aa7","company_id":"8ad90b6d-bf79-48ab-8c9a-847327f0009f","title":"Senior Staff AI Security Lead","slug":"senior-staff-ai-security-lead-fbe42f3c","description":"About IonQ:  \n IonQ, Inc . [NYSE: IONQ] is the world’s leading quantum platform and merchant supplier - delivering integrated quantum solutions across computing, networking, sensing, and security. IonQ’s newest generation of quantum computers, the IonQ Tempo, is the latest in a line of cutting-edge systems that have been helping customers and partners including Amazon Web Services, and AstraZeneca achieve 20x performance results and accelerate innovation in drug discovery, materials science, financial modeling, logistics, cybersecurity, and defense. In 2025, the company achieved 99.99% two-qubit gate fidelity, setting a world record in quantum computing performance . Headquartered in College Park, Maryland, IonQ has operations in California, Colorado, Massachusetts, Tennessee, Washington, Italy, South Korea, Sweden, Switzerland, Canada, and the United Kingdom. Our quantum computing services are available through all major cloud providers, while we also meet the needs of networking and sensing customers across land, sea, air, and space. IonQ is making quantum platforms more accessible and impactful than ever before.  \n Location: This role can work onsite or hybrid from one of our offices. We are open to a fully remote option for the right candidate. Travel: Up to 10% Job ID: 1915\n The Role:  \n We are hiring a senior technical leader to build the AI foundation our security organization runs on — and to make sure it actually gets used. This is a dual-mandate role. You will stand up the enterprise AI infrastructure that lets security teams safely build with large language models and agents, and you will lead the adoption effort that turns that infrastructure into measurable gains across detection and response, threat intelligence, vulnerability management, GRC, and security engineering.\n This is a hands-on senior individual contributor role. You will write code, design systems, and set technical direction. You will not manage a team, but you will lead through influence: partnering with engineering leaders, coaching analysts and engineers, and building the coalitions that make org-wide change stick. Expect roughly an even split between platform building and enablement work.\n You will report to the security leadership team and work closely with Platform Engineering, IT, Legal, Privacy, and the broader AI/ML organization.\n Responsibilities :\n Stand Up the Enterprise AI Infrastructure \n \n Build the platform. Design and build the shared platform security teams use to develop AI applications — model gateway and routing, authentication and authorization, secrets handling, rate limiting, cost attribution, and audit logging.\n Enable agentic systems. Establish patterns and reusable components for agentic workflows: tool and MCP server integration, sandboxed execution, human-in-the-loop approval gates, and least-privilege scoping for agent credentials.\n Wire in the data. Connect security's knowledge — runbooks, detection logic, past incidents, asset inventory, policy documents — to AI systems through well-governed retrieval pipelines with correct access controls and data classification enforcement.\n Make it measurable. Build the evaluation harness, regression suites, and observability that tell us whether an AI system is working: output quality, latency, cost, hallucination rate, and drift over time. Nothing ships to production without a baseline.\n Secure the stack itself. Implement guardrails against prompt injection, data exfiltration through model outputs, insecure tool use, and supply-chain risk in models and AI dependencies. Red-team the platform and the applications built on it.\n Set the standards. Own the reference architecture, golden paths, and internal documentation so that a security engineer can go from idea to reviewed prototype in days rather than months.\n \n Drive AI Adoption Across the Security Organization \n \n Find the real use cases. Work directly with detection engineering, IR, threat intel, vulnerability management, GRC, and security operations to identify where AI meaningfully reduces toil or improves quality — and where it does not. Kill weak ideas early.\n Build the flagship applications. Deliver a small number of high-visibility wins yourself. Nothing drives adoption like a working tool that saves an analyst two hours a day.\n Teach the organization. Run enablement programs — office hours, workshops, internal documentation, prompt and agent design patterns, brown-bags — that raise the AI fluency of the entire security organization, not just the engineers.\n Measure adoption honestly. Define and report the metrics that show whether adoption is real: active users, workflows automated, analyst hours returned, time-to-detect and time-to-respond improvements, quality deltas against human baselines.\n Establish governance that enables. Partner with Legal, Privacy, and Compliance to establish the acceptable-use policy, review process, and risk framework for AI in security workflows — des","salary_min":180000,"salary_max":225000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["api-design","cloud","embeddings","llm","distributed-systems","agents","fine-tuning","security"],"apply_url":"https://ionq.com/job?gh_jid=6208057004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T20:15:43Z","expires_at":"2026-11-13T17:02:18.061051Z","created_at":"2026-10-14T15:24:42.975766Z","updated_at":"2026-10-14T17:02:18.064019Z","company_name":"IonQ","company_slug":"ionq","company_logo_url":"https://www.google.com/s2/favicons?domain=ionq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a54be057-d871-4092-a1aa-9f775c325aa7"},{"id":"074ea5ff-c6b6-449e-9c6e-56c3323c5341","company_id":"d8e15a46-b80d-4228-8e7b-34f00357f377","title":"Lead Data Engineer","slug":"lead-data-engineer-5087a7dc","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 \n  \n We're looking for a Lead Data Engineer to join our Data Engineering \u0026 Architecture team. In this role, you'll help grow a Data Platform that makes it easier for teams across our company to work with data confidently and  effectively . Your work will directly support our business goals not just by keeping data flowing, but by turning it into something people can actually use and trust. \n \n  \n This is a great opportunity for someone who enjoys both the technical craft of data engineering and the human side of it: collaborating with partners, sharing knowledge, and helping others get more out of the tools and systems you build  together . \n  \n \n What You Will Be Doing \n  \n \n You'll design, maintain, and improve the pipelines and infrastructure that power our data platform. Day to day, that means working across the full data lifecycle — from ingestion and streaming to transformation and delivery — using tools like Apache Airflow, Spark, Iceberg, Kafka, Flink, and dbt Labs. \n \n  \n Beyond the technical work, you'll spend meaningful time with stakeholders to understand what they need and help define how data should flow across systems. That includes co-creating data contracts, writing clear documentation, and guiding teammates on how to get the most out of the platform. \n \n  \n A few other things you'll take on: \n \n \n Monitor and refine pipeline performance and refine processes to keep things running smoothly. \n \n Use Terraform to provision and manage resources on Google Cloud Platform (GCP). \n \n Help steward the platform by reviewing code and ensuring good practices and standard processes are followed. \n \n Process access requests and support compliance with platform policies. \n \n What You Bring \n \n \n 8+ years of experience in data engineering or related roles \n \n Experience with Apache Airflow for orchestrating data workflows \n \n Experience with Spark, Flink, Kafka, and dbt Labs for data processing and transformation \n \n Understanding of Apache Iceberg for data table formats and data warehouse and lakehouse strategies \n \n Skilled in using Terraform for infrastructure-as-code deployments \n \n Proficient in programming languages, particularly Bash, Python, and SQL \n \n Knowledge of database systems, including OLAP, OLTP, Document, and Vector databases \n \n Experience with Google Cloud Platform (GCP) for cloud resource management \n \n Bonus: Proven experience with data security and access controls for SOX Compliant data \n \n Bonus: Experience and knowledge of the Elastic stack \n \n Compensation for this role is in the form of base salary.  This role does not have a variable compensation component.   \n The typical starting salary range for new hires in this role is listed below.  In select locations (including Seattle WA, Los Angeles CA, the San Francisco Bay Area CA, and the New York City Metro Area), an alternate range may apply as specified 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 pay more or less than the posted range, and the ranges may be modified in the future.   \n An employee's position within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs. \n Elastic believes that employees should have the opportunity to share in the value that we create together for our shareholders. Therefore, in addition to cash compensation, this role is currently eligible to participate in Elastic's stock program.  Our total rewards package also includes a company-matched 401k with dollar-for-dollar matching up to 6% of eligible earnings, along with a range of other benefits offered with a holistic emphasis on employee well-being. \n The typical starting salary range for this role is:\n $113,100 — $178,900 USD \n The typical starting salary range for this role in the select locations listed above is:\n $135,900 — $215,100 USD \n Additional Information - We Take Care of Our People \n As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance grea","salary_min":135900,"salary_max":215100,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["embeddings","search","data-pipeline","data-engineering"],"apply_url":"https://jobs.elastic.co/jobs?gh_jid=8247144\u0026gh_jid=8247144","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T18:39:27Z","expires_at":"2026-11-13T16:57:57.540935Z","created_at":"2026-10-14T15:21:37.619706Z","updated_at":"2026-10-14T16:57:57.545432Z","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/074ea5ff-c6b6-449e-9c6e-56c3323c5341"},{"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":"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":"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":"f2901983-d0f7-4d0f-b542-5f56bf91d234","company_id":"ab3e4567-6f87-4ccf-9ec0-81fd82105f48","title":"Senior Partner Engineer","slug":"principal-partner-engineer-4e15c914","description":"About Us\n At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company.\n At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a \"normalized\" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in.\n Available Locations: Miami FL, Atlanta GA, Charlotte NC, Raleigh NC (Southeast Territory) \n Role Overview \n The  Partner Engineer (PE) is a high-impact, strategic technical sales leader  responsible for end-to-end technical lifecycle and enablement of the assigned partners, managing both pre-sales and post-sales engineering activities. In this role, you will act as the single technical point of contact as the trusted advisory for your assigned partners, driving technical validation during the sales cycle, architecting their delivery readiness and ensuring ongoing Partner Success. \n The role focuses heavily on driving value-based solution selling to accelerate the adoption of Cloudflare platform with focus on Cloudflare One (SASE/SSE) and AI Infrastructure (Workers AI / AI Gateway). By cultivating deeply technical champions within the partner organization, aligning architectures to tangible business outcomes, optimizing post-sales delivery and building joint managed services offering, you will directly expand and influence the pipeline to maximize Partner-Influenced Opportunity (PIO) revenue.\n Key Responsibilities \n 1. Pre-Sales Engineering \u0026 Champion Building \n \n \n Technical Alignment, Discovery \u0026 Enablement: Own the technical relationship with Partners to align on building joint Go-To-Market (GTM) strategies. Align, build, and accelerate partner technical capabilities, enabling them to lead deep technical discovery and architectural design to independently sell scalable solutions leveraging the Cloudflare Platform with a strong focus on AI and Cloudflare One (CF1).\n Technical Account Plan Ownership: Own and drive the end-to-end Technical Account Plan (TAP) for assigned strategic partners. Act as the primary architect mapping partner business goals to technical execution milestones, ensuring continuous alignment on CF1 and AI adoption targets.Building Technical Champions: Identify, enable, and cultivate key technical advocates and \"Champions\" within the partner organization to scale Cloudflare expertise independently, generate Partner-Influenced Opportunities (PIO), and empower them to deliver high-value professional services around Zero Trust and AI application security.Driving PIO via Next-Gen Architectures: Collaborate with sales teams to proactively source, qualify, and expand PIO through strategic architectural alignment, focusing on migrating legacy network architectures to CF1 and integrating secure AI solutions.Deal Support \u0026 Qualification: Enable partners' technical validation, proof-of-concept (PoC), demo, pipeline generation, and pipeline acceleration capabilities. Identify joint pursuit accounts, facilitate strategic technical account planning, and deliver cutting-edge solution architecture sessions for Named Partners \n \n 2. Accelerate Services Delivery \n \n \n ASD Partner Journey Ownership: Own the ASD partner journey to ensure partners possess the advanced capabilities required to deploy, manage, and support the Cloudflare platform and AI application workloads seamlessly.\n \n Joint Services Offering Acceleration with strong focus on CF1 and A1 : Build and co-develop joint services catalogues with key partners, accelerating the creation and launch of their own Cloudflare-focused services offerings, managed CF1 SASE practices, and AI security assessments.\n \n Service Monetization \u0026 Performance Metrics: Enable partners to embed CF1 and Workers AI capabilities into th","salary_min":212000,"salary_max":292000,"location":"Distributed","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","llm","embeddings","agents","security"],"apply_url":"https://boards.greenhouse.io/cloudflare/jobs/8173956?gh_jid=8173956","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-11T19:23:51Z","expires_at":"2026-11-13T16:58:05.40917Z","created_at":"2026-09-12T13:39:07.481516Z","updated_at":"2026-10-14T16:58:05.412091Z","company_name":"Cloudflare","company_slug":"cloudflare","company_logo_url":"https://www.google.com/s2/favicons?domain=cloudflare.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f2901983-d0f7-4d0f-b542-5f56bf91d234"},{"id":"dbcf2dbd-4afa-40e0-9ec9-3c0155933fff","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Machine Learning Engineer, Public Sector","slug":"senior-machine-learning-engineer-public-sector-1fe84597","description":"The goal of a Senior Machine Learning Engineer at Scale is to own how we apply generative AI, agentic AI, computer vision, and reinforcement learning to mission-critical problems in production. Our senior machine learning engineers are handed problems that don't yet have an established approach, they propose the architecture, build it with support from other engineers, and are accountable for whether it holds up in the environments our customers depend on.\n Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like  Donovan  and  Thunderforge . Our work spans multiple modalities, with our primary focus on agentic systems built on large language models. We are developing agents that solve complex operational and planning challenges for government partners: agent frameworks that integrate custom retrieval pipelines and production APIs, memory and context-management systems that hold state across long-running tasks, geospatial reasoning over maps and spatial data, and the evaluation tooling that benchmarks and refines agent behavior. We also apply reinforcement learning in targeted places where it earns its keep, and our computer vision work advances evaluation, labeling efficiency, and multimodal model training in support of defense applications.\n As a Senior MLE, you'll have design authority over a capability area - the final say on the patterns used within your team, and the responsibility to make those patterns work under real constraints: classified environments, limited compute, and correctness requirements that don't bend.\n You will:\n \n Own the design and delivery of agent capabilities end to end - architecture, implementation, and the evaluation that proves they work\n Define net-new patterns in problem spaces with no established approach, propose them to the wider team, and lead the work to build them\n Take state of the art models developed internally and from the community and put them into production to solve problems for our customers and taskers\n Improve and maintain production models and agents through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics\n Build agent-level evaluation benchmarks, LLM judges, and verifiers - and use it to hillclimb performance rather than just report on it\n Partner with product and research teams to scope and shape high-impact initiatives, including for upcoming product lines\n Build scalable machine learning infrastructure to automate and optimize our ML services\n Work directly with government users and subject-matter experts, and translate what you learn into technical direction\n Act as a force multiplier and a primary reviewer for your team, mentoring at least one engineer, and your manager's go-to on feasibility questions\n Communicate technical tradeoffs clearly to non-technical stakeholders\n Treat security and compliance as design constraints to engineer around rather than blockers to route past\n Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations\n Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment\n Comfortable with light travel (approximately 10%) for customer interaction and team needs\n \n This role will require an active TS security clearance \n Ideally You'd Have:\n \n 5+ years  of experience building and deploying applied ML systems in production environments\n Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment\n A track record of owning architectural decisions and defending the tradeoffs behind them - not just implementing a design handed to you\n Experience shipping agentic systems with real production traffic and evaluation rigor, rather than prototypes or demos\n Solid background in algorithms, data structures, and object-oriented programming\n Strong programming skills in Python, experience in PyTorch or Tensorflow\n Experience mentoring or reviewing the work of other engineers\n \n Nice to Haves:\n \n Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization\n Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments\n Experience with computer vision, generative AI models, large language models, or agentic systems\n Familiarity with ML evaluation frameworks and agentic model design\n Experience deploying ML in classified, air-gapped, or IL5+ environments\n Geospatial or GEOINT experience\n Inference optimization experience \n Fine-tuning experience: SFT, RL, or embedding models\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","salary_min":196000,"salary_max":294000,"location":"Denver, CO","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","deep-learning","embeddings","cloud","computer-vision","reinforcement-learning","fine-tuning","llm"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4732798005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-10T17:36:42Z","expires_at":"2026-11-13T16:55:03.283011Z","created_at":"2026-09-12T13:31:38.943747Z","updated_at":"2026-10-14T16:55:03.286184Z","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/dbcf2dbd-4afa-40e0-9ec9-3c0155933fff"},{"id":"9a398fdb-238a-481a-bd92-943d478410ae","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Software Engineer II, AI Labs \u0026 Foundations","slug":"senior-software-engineer-ii-ai-labs-foundations-a4264177","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview\n Join Instacart's mission to transform grocery shopping through frontier AI. As a Senior Software Engineer on AI Labs \u0026 Foundations, you will design, build, and operate the high-scale production systems that power our most ambitious AI experiences—from Cart Assistant, our conversational shopping agent, to voice AI interactions and beyond. This is a high-impact opportunity to work at the intersection of robust software engineering and cutting-edge production AI/ML, directly shaping products used by millions of customers every day.\n We are hiring a Senior Software Engineer who will participate in the design and delivery of production AI systems, identify high-leverage technical opportunities, and contribute hands-on to AI-native products across Instacart's platform. We value bottom-up ideas, high engineering quality, and close partnership with Product, Data Science, ML, and Infrastructure teams. If you enjoy inventing, navigating ambiguity, prototyping fast, and turning wild ideas into real, scalable products, this is the team for you.\n AI Labs \u0026 Foundations sits at the intersection of frontier AI research and production engineering. Our portfolio spans the full stack of AI innovation at Instacart, including building and launching Cart Assistant, pioneering voice AI interactions, and constructing the foundational systems that power these cutting-edge experiences. We are a fast-moving, collaborative team that thrives on 0-to-1 thinking, shares learnings openly, and ships with urgency by prototyping fast and testing rigorously.\n About the Job\n \n Design, build, and operate production AI-powered systems and agentic experiences (including Cart Assistant and voice AI) that directly impact how millions of customers shop.\n Build foundational systems for cutting-edge AI experiences, ranging from embedding infrastructure and voice AI pipelines, to client facing components and integrations, by prototyping bold ideas and productizing what works.\n Integrate foundation models via APIs and open-source frameworks; apply techniques like retrieval-augmented generation and vector search where appropriate.\n Own projects end-to-end: requirements, technical design, implementation, testing, deployment, observability, and iterative improvement focused on reliability, latency, and cost efficiency.\n Collaborate with cross-functional partners in product, design, data science, and infrastructure to ship AI features end-to-end.\n Drive engineering excellence, including thoughtful system design, rigorous code review, and technical leadership that includes defining and promoting best practices for AI/ML production engineering across the team.\n \n About You\n Minimum Qualifications: \n \n Proven senior software engineer who has built, shipped, and operated production systems at scale. You make architectural calls, own what you build, and deliver through ambiguity.\n Hands-on experience with AI or ML in production. You've shipped LLM-powered features or integrated foundation model APIs into a live product, demonstrating the necessary expertise at the intersection of robust software engineering and deep production ML.\n Experience owning services end-to-end, including CI/CD, automated testing, observability (logging, metrics, tracing), and on-call participation.\n Strong communicator who partners well across disciplines - you want to get to the right answer, not just defend the first one.\n Golang, Python, or Typescript language experience \n Excitement and ability to leverage cutting-edge development tools, including AI assistance (e.g., Copilot, Cursor, Claude), to maximize velocity.\n \n Preferred Qualifications: \n \n 5 to 8+ years of industry experience.\n A track record of 0-to-1 work taking unconventional ideas from prototype through rapid iteration to production.\n Experience building conversational agents, multi-tur","salary_min":192000,"salary_max":202500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["embeddings","llm","agents","speech","search","generative-ai","rag","code-generation"],"apply_url":"https://instacart.careers/job/?gh_jid=8053797","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-02T21:03:54Z","expires_at":"2026-11-13T16:57:55.635503Z","created_at":"2026-09-03T13:40:00.251564Z","updated_at":"2026-10-14T16:57:55.638788Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9a398fdb-238a-481a-bd92-943d478410ae"},{"id":"dd18eb8b-8f4a-4d9d-893d-575ef65b5c26","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Software Engineer - AI/Agent Engineer, Care Automation","slug":"staff-software-engineer-aiagent-engineer-care-automation-f566a4c4","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n The Care Automation team builds the systems and automations that power how Toast supports its customers. Our work connects the platforms, workflows, and integrations that our Customer Care Agents rely on every day to resolve issues quickly and deliver a great customer experience.\n We are looking for an AI/Agent Engineer to join this team and help define how intelligent automation changes the way care operations work at Toast. You will be at the center of building the AI layer across our care systems, bringing together large language models, agentic frameworks, and workflow automation to reduce manual effort and improve outcomes for both agents and customers.\n In this role, you will design and build AI-powered automations that work across our Care tool suite, including Salesforce Service Cloud and Amazon Connect. You will partner closely with software engineers, Salesforce developers, and operations stakeholders to ship production-grade AI systems that make a measurable difference for our Customer Care teams and customers.\n  \n A day in the life (Responsibilities) \n \n Design, build, and operate AI-powered automations and agentic workflows that help Customer Care Agents resolve issues faster and more consistently\n Build and maintain integrations between LLM-based systems and our care technology stack, including Salesforce, Amazon Connect, and iPaaS workflow automation platforms\n Prototype and iterate on agentic solutions that can take autonomous actions across care systems, with appropriate human-in-the-loop checkpoints\n Write automated tests and participate in code reviews to maintain code quality and system reliability\n Monitor AI system performance, respond to incidents, and iterate based on operational data and agent feedback\n Partner with software engineers, Salesforce developers, and operations stakeholders to translate care workflows into intelligent, automated solutions\n Leverage AI-augmented development tools to increase development velocity and code quality\n \n  \n What you'll need to thrive (Requirements) \n \n 5+ years of software engineering experience, with meaningful hands-on experience building AI/ML systems in production\n Practical experience with large language models, agentic frameworks (such as LangChain, LangGraph, or similar), and prompt engineering\n Strong backend engineering skills in Python, Java, or Go\n Experience with event-driven architectures and building integrations across enterprise systems\n Ability to evaluate trade-offs between automation accuracy, latency, and cost in production AI systems\n Bachelor's degree in Computer Science or a related field, or equivalent practical experience\n \n  \n Bonus Ingredients (Nice to Have) \n \n Experience with contact center platforms such as Salesforce Service Cloud or Amazon Connect\n Experience with Workato or another iPaaS platform\n Hands-on experience with RAG architectures and vector databases\n Familiarity with AWS AI/ML services (Bedrock, SageMaker, Lambda)\n \n  \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-Remote\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. You can learn more about how we align pay with local labor markets in our Geographic Pay Zone Philosophy . \n Zone A\n $193,000 — $309,000 USD \n Zone B\n $168,000 — $269,000 USD \n Zone C\n $151,000 — $242,000 USD \n How Toast Uses AI in its Hiring Process \n Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focused on your conversation. All hiring decisions are made by people. W","salary_min":151000,"salary_max":242000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","llm","rag","embeddings","cloud"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8147449","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-01T17:24:23Z","expires_at":"2026-11-13T16:58:22.399426Z","created_at":"2026-09-02T13:40:37.560409Z","updated_at":"2026-10-14T16:58:22.403798Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dd18eb8b-8f4a-4d9d-893d-575ef65b5c26"},{"id":"5cda7f2c-d00e-45ae-bf4a-b6e6811a6e5a","company_id":"7f070aa1-7d20-4bbe-b6d2-68769923074e","title":"Principal Enterprise Technology Architect","slug":"principal-enterprise-technology-architect-e98bf7db","description":"Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.\n\n\n\nWe're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.\n\n\n\nWe're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.\n\n\n\nIf you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.\n\n\n\nAbout the Role:\n\nWe are looking for a Principal Enterprise Technology Architect to own the technical vision for how our internal business systems connect, scale, and evolve. This is a newly created senior Individual Contributor role: you will be the highest-ranking technical voice in our Enterprise Technology organization, setting the architectural standard that guides our team of experienced system administrators.\n\n \n\nCrusoe is at an inflection point. We have mature, capable platforms running Finance (Oracle Fusion, Coupa, Sage Intacct), Construction and Operations (Procore, Oracle Primavera), and People (Rippling, Ashby), but they operate largely as islands. Your mandate is to design the unified architecture that connects them: defining master data models, engineering a scalable and increasingly event-driven integration layer, and building the governance frameworks (including how AI is responsibly introduced into these systems) that take us from startup agility to enterprise maturity.\n\n \n\nThis is a greenfield architecture role first. You will spend the majority of your time designing systems, producing ERDs, context diagrams, and sequence flows, before a single line of configuration is written. This role does not carry formal people-management responsibility, though as a Principal (P6) you may guide contributors indirectly on complex initiatives. You will serve as the integration and architecture subject matter expert that a team of senior administrators pulls in on complex, cross-system initiatives, and as a voice in Architecture Review Board discussions on major platform changes.\n\n \n\nThis role partners with, rather than replaces, existing domain owners: the IT Architect retains ownership of iPaaS design and governance, the Identity Lead retains ownership of IAM/PAM and access governance, and the AI \u0026 Automation Staff Engineer retains ownership of AI governance and architecture. This role's mandate is enterprise-wide data-model and integration-pattern design, done in partnership with those owners.\n\n\n\nWhat You'll Be Working On:\n\n \n\n - Greenfield Architecture \u0026 System Design\n   \n   - Lead technical discovery for all major system initiatives, producing high-fidelity artifacts — ERDs, system context diagrams, data flow maps, and sequence diagrams — that serve as the source of truth for implementation\n   \n   - Assess current platform limitations and define the path forward, including when to optimize, when to re-platform, and when to build a custom integration layer\n   \n   - Serve as the primary technical evaluator during software procurement, vetting vendors on API robustness, data model flexibility, and security compliance — not just features\n   \n   - Identify and document integration debt across the current-state architecture and develop a prioritized roadmap to address it\n   \n   - Participate in Architecture Review Board discussions, ensuring major system changes align with enterprise standards and reference architecture\n\n - Master Data \u0026 Integration Strategy\n   \n   - Partner with platform SMEs and administrators to define a unified approach to master data across the organization\n   \n   - Partner with the IT Architect and platform SMEs who own our iPaaS integration environment today, co-developing reusable integration patterns, error-handling frameworks, and self-healing workflows that raise the bar without displacing existing ownership\n   \n   - Drive integration architecture toward API-first, event-driven patterns (REST, webhooks/pub-sub) where they improve reliability and reduce latency over batch processing\n   \n   - Define how internal systems authenticate and communicate, with strict enforcement of secure API standards (OAuth 2.0, REST/SOAP) across all integrations\n\n - AI \u0026 Intelligent Automation\n   \n   - Partner with t","salary_min":225000,"salary_max":255000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["agents","llm","cloud","embeddings","code-generation"],"apply_url":"https://jobs.ashbyhq.com/crusoe/582f3d5e-2ee6-47b7-9cfc-a9691cfa4bca/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T23:24:29.385Z","expires_at":"2026-11-13T16:56:14.653628Z","created_at":"2026-08-29T13:36:18.423689Z","updated_at":"2026-10-14T16:56:14.656554Z","company_name":"Crusoe","company_slug":"crusoe","company_logo_url":"https://www.google.com/s2/favicons?domain=crusoe.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5cda7f2c-d00e-45ae-bf4a-b6e6811a6e5a"},{"id":"526d6f5c-223d-4ef5-8e97-5b3b5234b42b","company_id":"9bd78d17-fe7f-46b8-82c9-7c3499c319cd","title":"Education Engineer ","slug":"education-engineer-14412907","description":"About Arize\n AI is rapidly transforming the world. As generative AI reshapes industries, teams need powerful ways to monitor, troubleshoot, and optimize their AI systems. That’s where we come in. Arize AI is the leading AI \u0026 Agent Engineering observability and evaluation platform , empowering AI engineers to ship high-performing, reliable agents and applications. From first prototype to production scale, Arize AX unifies build, test, and run in a single workspace—so teams can ship faster with confidence.\n We’re a Series C  company backed by top-tier investors,   with  over $135M in funding  and a rapidly growing customer base of  150+ leading enterprises and Fortune 500 companies.  Customers like  Booking.com , Uber, Siemens, and PepsiCo leverage Arize to deliver AI that works.\n Note: Arize is a remote-first company but certain roles often appear at in-person events. The nature of this role requires candidates to be based in the San Francisco Bay metro area, though there isn't an in-office requirement. \n The Opportunity \n AI is rapidly transforming the world. Whether it's developing the next generation of human-level intelligence, enhancing voice assistants, or enabling researchers to analyze genetic markers at scale, AI is increasingly integrated into various aspects of our daily lives. Arize AI is the leading AI observability and evaluation platform, empowering AI engineers to build and deploy high-performing, reliable models. As the AI landscape shifts from traditional ML to generative AI and agentic systems, Arize ensures teams have the tools to monitor, troubleshoot, and improve AI in production.\n The Role \n We are looking for an AI Education Engineer focused on developer growth. This team fits in developer advocacy in the product team, outside of core engineering, connecting product to the AI Engineer ecosystem. This role is ideal for people with an entrepreneurial mindset and passion to grow our developer community through code, education, content, and hack-a-thons. You will be joining a team of engineers, product and marketing with experience building the next generation of AI systems from agents to assistants to LLM applications.  \n What You'll Do \n \n Write code and build examples that our community can reference - agents \u0026 assistants in different frameworks\n Create awareness through technical content that educates AI developers – e.g. Github repos, blogs, eBooks, product documentation, workshops, demos\n Build out Claude code and Cursor skills for automating systems\n Improve the growth marketing experience \n Be the “voice of the AI engineer” across Arize’s base of users and the AI developer community at large\n Serve as a product advocate for Arize's core SaaS or OSS offerings. Distill market insights and developer feedback to inform roadmap and GTM activities\n Craft best practices and novel perspectives on troubleshooting and improving production ML models and LLM-powered applications\n Plan and speak at Arize-hosted and industry events and conferences\n \n Who We're Looking for \n \n Working knowledge of generative AI\n Technical experience with the LLM app development stack (e.g. agents, orchestration frameworks, vector databases, evaluation libraries) with proficiency in Python\n Engaged in technical communities via GitHub, Medium, DZone, Stack Overflow, Reddit, LinkedIn, etc.\n A data-driven creative who thrives in ideating and delivering on novel activities that earn the attention and trust of AI/ML practitioners\n A self-learner who isn’t daunted by technical complexity and welcomes the challenge to develop their own POV\n Excellent written and verbal communication\n Willingness to attend evening meetups and events in the SF Bay Area\n \n The estimated annual salary for this role is between $100,000 - $175,000, plus a competitive equity package. Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes a comprehensive benefit package, including: medical, dental, vision, 401(k) plan, unlimited paid time off, generous parental leave plan, and others for mental and wellness support.\n While we are a remote-first company, we have opened offices in New York City and the San Francisco Bay Area, as an option for those in those cities who wish to work in-person. For all other employees, there is a WFH monthly stipend to pay for co-working spaces.\n More About Arize\n Arize’s mission is to make the world’s AI work—and work for people. Our founders came together through a shared frustration: while investments in AI are growing rapidly across every industry, organizations face a critical challenge—understanding whether AI is performing and how to improve it at scale.\n Learn more about what we're doing here:\n https://techcrunch.com/2025/02/20/arize-ai-hopes-it-has-first-mover-advantage-in-ai-observability/ \n https://arize.com/blog/arize-ai-raises-70m-seri","salary_min":100000,"salary_max":175000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"mid","tags":["agents","generative-ai","llm","embeddings"],"apply_url":"https://job-boards.greenhouse.io/arizeai/jobs/6128123004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T20:45:15Z","expires_at":"2026-11-13T16:55:34.218021Z","created_at":"2026-10-14T15:19:24.33629Z","updated_at":"2026-10-14T16:55:34.221198Z","company_name":"Arize AI","company_slug":"arize-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=arize.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/526d6f5c-223d-4ef5-8e97-5b3b5234b42b"},{"id":"e85b9a35-40f0-48b2-addc-33f4f2ea70b8","company_id":"9bd78d17-fe7f-46b8-82c9-7c3499c319cd","title":"Applied AI Engineer","slug":"applied-ai-engineer-6bcaee2f","description":"About Arize\n AI is rapidly transforming the world. As generative AI reshapes industries, teams need powerful ways to monitor, troubleshoot, and optimize their AI systems. That’s where we come in. Arize AI is the leading AI \u0026 Agent Engineering observability and evaluation platform , empowering AI engineers to ship high-performing, reliable agents and applications. From first prototype to production scale, Arize AX unifies build, test, and run in a single workspace—so teams can ship faster with confidence.\n We’re a Series C  company backed by top-tier investors,   with  over $135M in funding  and a rapidly growing customer base of  150+ leading enterprises and Fortune 500 companies.  Customers like  Booking.com , Uber, Siemens, and PepsiCo leverage Arize to deliver AI that works.\n Note: Arize is a remote-first company but certain roles often appear at in-person events. The nature of this role requires candidates to be based in the San Francisco Bay metro area, though there isn't an in-office requirement. \n The Opportunity \n AI is rapidly transforming the world. Whether it's developing the next generation of human-level intelligence, enhancing voice assistants, or enabling researchers to analyze genetic markers at scale, AI is increasingly integrated into various aspects of our daily lives. Arize AI is the leading AI observability and evaluation platform, empowering AI engineers to build and deploy high-performing, reliable models. As the AI landscape shifts from traditional ML to generative AI and agentic systems, Arize ensures teams have the tools to monitor, troubleshoot, and improve AI in production.\n The Role \n We are looking for an AI Applied Engineer focused on developer growth. This team fits in developer advocacy in the product team, outside of core engineering, connecting product to the AI Engineer ecosystem. This role is ideal for people with an entrepreneurial mindset and passion to grow our developer community through code, education, content, and hack-a-thons. You will be joining a team of engineers, product and marketing with experience building the next generation of AI systems from agents to assistants to LLM applications.  \n What You'll Do \n \n Write code and build examples that our community can reference - agents \u0026 assistants in different frameworks\n Create awareness through technical content that educates AI developers – e.g. Github repos, blogs, eBooks, product documentation, workshops, demos\n Build out Claude code and Cursor skills for automating systems\n Improve the growth marketing experience \n Be the “voice of the AI engineer” across Arize’s base of users and the AI developer community at large\n Serve as a product advocate for Arize's core SaaS or OSS offerings. Distill market insights and developer feedback to inform roadmap and GTM activities\n Craft best practices and novel perspectives on troubleshooting and improving production ML models and LLM-powered applications\n Plan and speak at Arize-hosted and industry events and conferences\n \n Who We're Looking for \n \n Working knowledge of generative AI\n Technical experience with the LLM app development stack (e.g. agents, orchestration frameworks, vector databases, evaluation libraries) with proficiency in Python\n Engaged in technical communities via GitHub, Medium, DZone, Stack Overflow, Reddit, LinkedIn, etc.\n A data-driven creative who thrives in ideating and delivering on novel activities that earn the attention and trust of AI/ML practitioners\n A self-learner who isn’t daunted by technical complexity and welcomes the challenge to develop their own POV\n Excellent written and verbal communication\n Willingness to attend evening meetups and events in the SF Bay Area\n \n The estimated annual salary for this role is between $100,000 - $175,000, plus a competitive equity package. Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes a comprehensive benefit package, including: medical, dental, vision, 401(k) plan, unlimited paid time off, generous parental leave plan, and others for mental and wellness support.\n While we are a remote-first company, we have opened offices in New York City and the San Francisco Bay Area, as an option for those in those cities who wish to work in-person. For all other employees, there is a WFH monthly stipend to pay for co-working spaces.\n More About Arize\n Arize’s mission is to make the world’s AI work—and work for people. Our founders came together through a shared frustration: while investments in AI are growing rapidly across every industry, organizations face a critical challenge—understanding whether AI is performing and how to improve it at scale.\n Learn more about what we're doing here:\n https://techcrunch.com/2025/02/20/arize-ai-hopes-it-has-first-mover-advantage-in-ai-observability/ \n https://arize.com/blog/arize-ai-raises-70m-serie","salary_min":100000,"salary_max":175000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"mid","tags":["agents","embeddings","generative-ai","llm"],"apply_url":"https://job-boards.greenhouse.io/arizeai/jobs/6128122004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T20:43:02Z","expires_at":"2026-11-13T16:55:34.207799Z","created_at":"2026-10-14T15:19:24.324895Z","updated_at":"2026-10-14T16:55:34.210821Z","company_name":"Arize AI","company_slug":"arize-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=arize.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e85b9a35-40f0-48b2-addc-33f4f2ea70b8"},{"id":"e17eda01-e317-4c5f-9dae-d8e3404b1b2e","company_id":"ec4a8bb4-3840-4054-8ccd-77e81db037af","title":"Data Scientist/Senior Data Scientist","slug":"data-scientistsenior-data-scientist-565352fb","description":"C3 AI (NYSE: AI), is the Enterprise AI application software company. C3 AI delivers a family of fully integrated products including the C3 Agentic AI Platform, an end-to-end platform for developing, deploying, and operating enterprise AI applications, C3 AI applications, a portfolio of industry-specific SaaS enterprise AI applications that enable the digital transformation of organizations globally, and C3 Generative AI, a suite of domain-specific generative AI offerings for the enterprise. Learn more at: C3 AI \n As a member of the C3 AI Data Science team , you will work with some of the largest companies on the planet helping them build the next generation of AI-powered enterprise applications on the C3 AI Platform. You will work directly with data scientists, AI engineers, and subject matter experts to design and deploy AI capabilities that give our customers the information they need to make better decisions and accelerate their digital transformation. You will identify the right AI approaches for each problem and implement them on the C3 AI Platform so they run reliably at enterprise scale.\n Qualified candidates will have deep knowledge of modern AI and ML techniques — including large language models, agentic systems, and classical statistical methods — along with a clear understanding of their limitations and how to adapt them to large-scale production environments. Some travel is expected.\n Note: This is a client-facing position. Data Scientists are expected to work on-site with our customers every week and should be comfortable traveling regularly to customer locations. \n Responsibilities: \n \n Lead the research, design, implementation, and deployment of AI models, agentic solutions, and optimization algorithms for enterprise applications on the C3 AI Platform.\n Partner with C3 AI customers to build and scale their own AI applications on the Platform.\n Contribute to the design and implementation of new AI capabilities within the C3 AI Platform.\n Analyze model performance across enterprise deployments, diagnose issues such as poor recall or false positive rates, and recommend targeted improvements.\n Collaborate with data engineers and subject matter experts from C3 AI and customer teams to source, validate, and correctly leverage new data assets.\n \n Qualifications: \n \n MS or PhD in Computer Science, Electrical Engineering, Statistics,   Operations Research, or a related field.\n Hands-on AI experience spanning generative AI, agentic systems, supervised and unsupervised learning, and classical regression and classification.\n Strong mathematical foundation in linear algebra, calculus, probability, and statistics.\n Experience building and deploying models at scale in distributed or cloud-native environments.\n Ability to drive projects independently and collaborate effectively across technical and non-technical teams.\n Sharp, motivated, and focused on making a real impact.\n Excellent verbal and written communication skills.\n \n Preferred Qualifications: \n \n Proficiency in Python; experience with JavaScript, Java, or Scala is a plus.\n Familiarity with LLM frameworks (e.g., LangChain, LlamaIndex), vector databases, or RAG architectures.\n A portfolio of AI projects (GitHub, publications, or open-source contributions) is a plus.\n C3 AI provides excellent benefits, a competitive compensation package and generous equity plan.  \n California Base Pay Range\n $136,000 — $183,000 USD \n C3 AI is proud to be an Equal Opportunity and Affirmative Action Employer. We do not discriminate on the basis of any legally protected characteristics, including disabled and veteran status.","salary_min":136000,"salary_max":183000,"location":"Redwood City, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["rag","generative-ai","embeddings","agents","llm","data-science"],"apply_url":"https://c3.ai/job-description/8751111002?gh_jid=8751111002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T17:07:29Z","expires_at":"2026-11-13T16:58:24.05679Z","created_at":"2026-08-27T13:40:52.550205Z","updated_at":"2026-10-14T16:58:24.060369Z","company_name":"C3 AI","company_slug":"c3-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=c3.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e17eda01-e317-4c5f-9dae-d8e3404b1b2e"},{"id":"7cfc8bd1-ee86-44f8-a76b-20b093825232","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior IT Platform and Automation Engineer","slug":"it-platform-and-automation-engineer-56eeaeee","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 Job Description\n We're seeking a Senior IT Platform \u0026 Automation Engineer who treats internal infrastructure like a product—not a firefighting exercise. This is not a traditional helpdesk or \"break-fix\" role. You'll be the architect behind our internal employee experience, acting as the connective tissue between our office network, collaboration services, security stack, and core business systems (People Tech, CRM, and Engineering tools).\n If you prefer writing Python scripts over manually resetting passwords, and if you get excited about connecting Claude AI to Greenhouse via custom Slack workflows, we want to talk to you. \n This role is NOT: \n \n A traditional helpdesk position. You won't be resetting passwords or shipping laptops.\n A pure networking role. You'll touch networks, but automation is the priority.\n A machine learning engineer role. You'll experiment with AI, but you won't build vector databases or train models.\n \n   What You will Do\n Identity, Endpoint \u0026 Network (The Foundation) \n \n Identity \u0026 Access Management: Own our IdP (Okta/Auth0). Implement and automate SSO, MFA enforcement, and SCIM provisioning across all SaaS tools. Enforce zero-trust principles everywhere.\n Endpoint Lifecycle: Own the full hardware and MDM lifecycle. Automate device provisioning, configuration, and secure deployment using Jamf and Intune. Build zero-touch onboarding workflows. (Note: You will own the automation and security profiles; physical device shipping/fulfillment is handled by our IT Ops coordinators.) \n Network Engineering: Design, deploy, and maintain high-availability office networking (cloud-managed switching/routing, Wi-Fi) and secure remote access solutions (ZTNA/VPN) for a hybrid workforce. We prioritize automation-first engineers.\n \n Custom Integrations \u0026 Automation (The Builder Work) \n \n API Integrations: Architect and build custom integrations between our core SaaS stack—syncing data between Okta, Greenhouse, Notion, and our CRM to eliminate manual data entry.\n Workflow Orchestration: Own our internal automation layer. We currently use a mix of lightweight no-code tools (Zapier, App Script, AppSheet) and custom Python. We're actively phasing out low-code for mission-critical paths—you'll have permission to replace them with proper code. To be clear: this is a coding-heavy role. You'll spend the majority of your time writing code, not clicking in UIs. The no-code tools are placeholders—we expect you to replace or maintain them.\n Internal Tooling: Build and maintain internal dashboards and Slack apps that give employees self-service access to IT, HR, and facilities requests.\n \n AI \u0026 LLM Experimentation (The Innovation Work) \n \n AI Integrations: Review and assess connectors (MCP) if available. Otherwise, build lightweight API wrappers and prompt engineering pipelines to connect LLMs (Claude, OpenAI) to internal tools like Slack and Notion.\n R\u0026D Time: Dedicated time each sprint to experiment with new AI models and identify productivity gains for the company.\n \n Security Automation \u0026 Compliance (The Glue) \n \n Security Scripting: Write robust scripts (Python, Bash, PowerShell) to automate security monitoring, endpoint patch management, and compliance reporting.\n Alert Correlation: Architect centralized alerting pipelines by integrating EDR/SIEM webhooks into Slack via native integrations (CrowdStrike), filtering noise and surfacing only actionable security anomalies.\n Compliance Automation: Create automated audit trails, access reviews, and reporting to support SOC2 / ISO compliance efforts—no more manual spreadsheet work.\n \n Tech Stack Summary: Python, Okta/Azure AD, Jamf/Intune, Slack API, Greenhouse API, Notion API, Claude/OpenAI APIs, CrowdStrike, Meraki/cloud-networking, Google Workspace + GAM, Jira.\n  \n Who You Are\n  \n Foundation (Must-Have Baseline) \n \n Experience: 4+ years in IT Engineering, Systems Engineering, or Platform roles with a heavy emphasis on automation and APIs.\n Coding Proficiency: Advanced Python or JavaScript/Node.js. You treat your scripts like software—git, CI/CD, testing, and documentation are non-negotiable. Our current automation stack is built on Python. Familiarity with either is sufficient—we don't expect you to know both.\n API Fluency: Proven experience building custom integrations using REST/GraphQL APIs. You know how to read documentation, handle OAuth2, and parse JSON in your sleep.\n IdP \u0026 MDM Experience: Hands-on experience with Okta or Azu","salary_min":150000,"salary_max":220000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","embeddings","agents","llm","api-design"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6150440004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-21T18:28:13Z","expires_at":"2026-11-13T16:55:33.522709Z","created_at":"2026-08-25T18:27:04.327152Z","updated_at":"2026-10-14T16:55:33.525663Z","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/7cfc8bd1-ee86-44f8-a76b-20b093825232"},{"id":"95641443-c7f1-443a-b4d7-ef763360667d","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Machine Learning Engineer","slug":"machine-learning-engineer-a0756d7f","description":"MACHINE LEARNING ENGINEER\n\n \n\nYou'll build the ML behind Firecrawl: the models and the systems that serve them. That starts with search: training and shipping the ranking and relevance models for one of our fastest-growing products, then extending that work across extraction quality and LLM-driven features. You'll also own how we measure: A/B testing launches and building the experimentation frameworks the whole team ships against. If you ship models into production, whether your title says ML engineer or data scientist, this is for you.\n\n\n\nSalary Range: $250,000–$290,000 USD/year (SF) / $210,000–$224,000 CAD/year (Toronto)\n\nEquity Range: Competitive equity. Details shared during the process.\n\nLocation: San Francisco, CA (SF HQ) or Toronto, ON (Toronto Hub). Hybrid, onsite 3+ days a week.\n\nJob Type: Full-Time\n\nExperience: 3+ years building ML or data-heavy systems in production\n\nWork Authorization: Open to candidates already authorized to work in the US or Canada. In the US, we also support H-1B transfers, F-1 OPT and STEM OPT, TN (Canada and Mexico), and E-3 (Australia). We’re not able to sponsor new H-1B visas for candidates outside the US. For Canada, we’ll consider work permit sponsorship case by case through our Toronto Hub.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data. It's the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nIn September 2026, we raised a $75M Series B led by Smash Capital, and we're spending it building the largest repository of knowledge in the world. We hit 8 figures in ARR in year one and more than doubled it in year two. We have 187k+ GitHub stars, putting us in the top 40 repositories of all time, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves. No hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web. That library is called Alexandria, and it starts now.\n\n\n\n\nWHAT YOU'LL DO\n\n - Improve ranking and relevance for Firecrawl Search, from feature engineering to model training to production\n\n - Build and tune models for learning-to-rank, query understanding, and LLM-driven retrieval\n\n - Extend ML across Firecrawl's products: extraction quality, content classification, and evaluation of LLM-driven features\n\n - Mine query logs and behavioral data at scale to find where our products win and where they fail\n\n - Build the data pipelines that turn web-scale crawl and query data into training data and features\n\n - Work hands-on with platform, search, and cloud DevOps engineers to get models running fast and cheap in production\n\n - Design our testing strategy: the A/B testing frameworks and offline evaluation the team ships against\n\n - Partner on product launches across Firecrawl: define success metrics, run the experiments, and make the ship/no-ship call on evidence\n\n - Report on how releases perform post-launch and turn the findings into the next iteration\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've shipped ML models into production systems and owned them after launch: deploying, monitoring, and retraining them, not handing them off\n\n - You have real ranking or relevance-modeling experience: learning-to-rank, recommendations, or search quality\n\n - You're comfortable in large, data-heavy systems: query logs, pipelines, and datasets that don't fit in memory\n\n - You write production-quality code (Python at minimum) and can work inside a real backend codebase\n\n - You're rigorous about measurement. You've designed and analyzed A/B tests and know when a lift is real\n\n - You can communicate results clearly to the team: what shipped, what moved, and what to do next\n\n\n\n\nNICE TO HAVE\n\n - MLOps experience: MLflow, experiment tracking, model registries, or feature stores. Kubernetes is a plus\n\n - Experience building or standardizing an experimentation framework at a previous company\n\n - Experience with embedding models, vector retrieval, or LLM-based relevance evaluation\n\n - Experience evaluating LLM outputs at scale: quality scoring, structured-extraction accuracy, or agent behavior\n\n - Spark or similar large-scale data processing experience\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - A pure statistician or analyst who needs an engineering team to productionize their work\n\n - Someone who wants to specialize narrowly and hand off everything else\n\n - Someone who optimizes for process over shipping\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency bec","salary_min":210000,"salary_max":224000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["embeddings","data-pipeline","payments","mlops","agents","search","llm","machine-learning"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/72f9dc1d-65db-48c9-b3d9-c6ccdb997006/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T21:40:10.101Z","expires_at":"2026-11-13T17:00:14.01626Z","created_at":"2026-08-25T18:32:19.563403Z","updated_at":"2026-10-14T17:00:14.019998Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/95641443-c7f1-443a-b4d7-ef763360667d"},{"id":"42dee226-8fe7-4865-9781-15de96ca3cf0","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"Forward Deployed Engineer","slug":"forward-deployed-engineer-a6d8ef8d","description":"About Dialpad Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage. \n Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved. \n Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile. \n Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. \n We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. \n We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . \n Your role The Forward Deployed Engineer (FDE) is a customer-embedded delivery engineer responsible for turning qualified AI opportunities into live, production-ready agent deployments. This role sits at the intersection of customer workflow design, applied AI engineering, enterprise integration, and production rollout. The core mission is to close the last-mile gap between platform capability and measurable customer outcomes by taking shaped opportunities from scoping through implementation and into the first production shift. \n You will work directly with customer stakeholders, internal delivery teams, and product partners to design, build, evaluate, deploy, and stabilize AI agents in real operating environments. This is a highly customer-facing, hands-on role for someone who can translate ambiguity into working systems and can move fluidly between technical depth and business impact. \n What you’ll do \n \n Convert prioritized use cases and pre-sales assumptions into a clear delivery scope, technical plan, and phased implementation approach. \n Work directly with customer technical and operational teams to map real workflows, exception paths, approvals, escalation rules, and human-in-the-loop controls. \n Design and build customer-specific AI agent solutions, including orchestration logic, prompt architecture, retrieval flows, workflow rules, and decision logic. \n Implement integrations with customer data sources, APIs, SaaS platforms, CRMs, ERPs, and enterprise knowledge systems so agents can perform real work end to end. \n Develop evaluation methods, guardrails, and acceptance criteria that measure agent usefulness, reliability, safety, task success, and production readiness. \n Run iterative testing, failure analysis, and performance tuning to improve quality, groundedness, latency, cost, and operational effectiveness. \n Prepare solutions for production by addressing access controls, observability, rollout sequencing, exception handling, fallback behavior, and support readiness. \n Lead deployment activities through initial go-live and first-shift stabilization, including rapid issue resolution, workflow adjustments, and customer-facing hypercare. \n Document reusable implementation patterns, operational runbooks, known failure modes, and recommended next improvements to support scale and repeatability. \n Feed field learnings back into Product, Engineering, and transformation teams so future deployments become faster, safer, and more repeatable. \n Detailed solution scoping after deal alignment. \n Customer-specific implementation and agent behavior design. \n Technical integration across enterprise systems and data sources. \n Evaluation, iterative quality improvement, and production hardening. \n Go-live readiness and first-shift production stabilization. \n Executive value framing before opportunity qualification. \n Broad transformation-roadmap design across multiple business units. \n Portfolio-level governance program management. \n Long-term managed-service ownership after the initial deployment phase, unless the service model explicitly extends the role. \n \n Skills you'll bring \n \n Experience in a high-impact, customer-facing technical role such as Forward Deployed Engineering, Solutions Architecture, Technical Consulting, Deployment Engineering, or AI","salary_min":112400,"salary_max":154400,"location":"Anywhere, US","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"mid","tags":["fine-tuning","llm","embeddings","agents"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8734067002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:03:38Z","expires_at":"2026-11-13T17:03:50.11573Z","created_at":"2026-08-25T19:52:34.272555Z","updated_at":"2026-10-14T17:03:50.118955Z","company_name":"Dialpad","company_slug":"dialpad","company_logo_url":"https://www.google.com/s2/favicons?domain=dialpad.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/42dee226-8fe7-4865-9781-15de96ca3cf0"},{"id":"d29bfaad-c593-4c53-93b1-dff4b4c64a86","company_id":"adc4981a-d4ff-4939-952f-362f51e1291d","title":"Sr. Machine Learning Engineer","slug":"sr-machine-learning-engineer-3ba58cf5","description":"Our Mission: \n 6sense's mission is to multiply what matters: growth, retention, and efficiency.  We envision a future where companies, teams and people reach their full potential.\n Our People: \n People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging.  Every 6sensor plays a part in defining the future of our industry-leading technology.  6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers.  We want 6sense to be the best chapter of your career. \n \n About 6sense\n 6sense is Intelligence for Agentic GTM. We turn every signal — yours and ours — into intelligence that every team, tool, and AI agent can act on and trust. Every day, the 6sense Signalverse™ captures one trillion signals to power AI that pinpoints who’s ready to buy, how to engage them, and when to act. 6sense was named a Leader in The Forrester Wave™: Revenue Marketing Platforms for B2B, Q1 2026.\n The Opportunity\n We’re hiring a Senior Machine Learning Engineer to join our AI team, reporting directly to the Head of AI.\n Signals tell you what happened. Our job is to explain why — and that is the problem you will work on. You will build the intelligence that turns a trillion daily signals into cited, explainable answers about why an account matters, why now, and who is deciding. Your models power products customers use every day, including RevvyAI, our conversational GTM intelligence product, and reach their stack through our APIs and MCP server.\n This is a build-and-ship role, not a research role. You will own problems end to end, work directly with Product and Go-to-Market, and see your work reach customers. You’ll join a team distributed across the US and India, at a company where AI is the product rather than a feature.\n What You’ll Do\n \n Own machine learning problems end to end — from data exploration and modeling through deployment, monitoring, and iteration in production.\n Build NLP, LLM, and agentic systems at enterprise scale, including retrieval-based architectures and multi-agent workflows.\n Develop ranking, recommendation, prediction, and optimization models that are explainable rather than black-box.\n Partner with Product and Go-to-Market to turn ambiguous business problems into shipped capabilities.\n Improve the performance, scalability, and reliability of production ML systems, and help shape AI platform architecture.\n Explain your work clearly to technical and non-technical audiences, and engage with customers when needed.\n Mentor engineers and raise the bar for engineering excellence.\n \n What We’re Looking For\n Required\n \n 8+ years of industry experience building and deploying machine learning systems in production, with clear end-to-end ownership.\n Strong foundation in machine learning and applied statistics, with hands-on depth in NLP, transformers, embeddings, and retrieval-based systems.\n Practical experience with modern GenAI tooling such as LangGraph, LangChain, or Amazon Bedrock.\n Strong Python skills and experience building distributed ML pipelines on cloud infrastructure (AWS, Databricks, or equivalent).\n Solid grasp of feature engineering, model evaluation, and MLOps practices.\n A product mindset — you want to build AI products customers use, and you measure yourself on customer impact.\n Excellent communication: you can explain complex technical work clearly, tell the story of what you’ve built and why, and hold your own with product and business partners.\n Comfort with ambiguity and the judgment to drive execution independently.\n \n Nice to Have\n \n Experience with RAG architectures, vector databases, and prompt engineering.\n Hands-on work with PyTorch or TensorFlow.\n Background in B2B SaaS, enterprise AI products, or forward-deployed engineering — especially where you worked directly with complex customer data and delivered quickly.\n \n  \n Base Salary Range: $200,349.50 - $260,912.60. The base salary range represents the anticipated low and high end of the base salary range for this position. Actual salaries may vary and may be above or below the range based on various factors, including but not limited to work location and experience. The base salary is one component of 6sense’s total compensation package for this position. Other compensation may include a bonus program or commission plan, and stock options if approved by 6sense’s board. In addition, 6sense provides a variety of benefits, including generous health insurance coverage, life, and disability insurance, a 401K employer matching program, paid holidays, self-care days, and paid time off (PTO). #Li-remote \n Notice of Collection and Use of Personal Information for California Residents: California Recruitment Privacy Notice and Policy \n Our Benefits:   \n Full-time employees can ta","salary_min":200349,"salary_max":260912,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","cloud","nlp","rag","agents","tensorflow","embeddings","pytorch"],"apply_url":"https://boards.greenhouse.io/6sense/jobs/8064973?gh_jid=8064973","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T15:47:30Z","expires_at":"2026-11-13T16:58:42.018053Z","created_at":"2026-08-25T18:30:07.718512Z","updated_at":"2026-10-14T16:58:42.021249Z","company_name":"6sense","company_slug":"6sense","company_logo_url":"https://www.google.com/s2/favicons?domain=6sense.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d29bfaad-c593-4c53-93b1-dff4b4c64a86"}],"page":1,"per_page":20,"total":294,"total_is_exact":true,"total_pages":15}