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At Samsara, we are helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing — and we are excited to help digitally transform their operations at scale. \n Working at Samsara means you’ll help define the future of physical operations and be on a team that’s shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you’ll have the autonomy and support to make an impact as we build for the long term. \n About the role: \n Want to build the data foundation powering AI and self-serve analytics at scale? As part of Samsara's Marketing Data and Analytics (MDA) Engineering team, you will shape the technical roadmap alongside our BI and Data Science peers. You will architect core Databricks pipelines, Gold Layer tables, and semantic models, turning ambitious, ambiguous business challenges into high-value data products, autonomous agents, and cross-functional automation.\n This role is open to candidates residing in the US.\n You should apply if: \n \n You want to impact the industries that run our world: Your efforts will result in real-world impact – helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.\n You are the architect of your own career: If you put in the work, this role won’t be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment and master your craft in a hyper growth environment.\n You’re energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.\n You want to be with the best: At Samsara, we win together, celebrate together and support each other. You will be surrounded by a high-calibre team that will encourage you to do your best. \n \n In this role, you will: \n \n Partner on defining the strategy and multi-quarter roadmap for MDA's data and AI foundation.\n Architect and own Samsara's marketing data infrastructure: ETL/ELT pipelines, Databricks Gold Layer tables, and the core data models that power segmentation, targeting, automation, and analytics.\n Design the semantic layers, data dictionaries, and structured documentation that enable self-serve BI and conversational analytics, partnering with the BI team to ensure core sales and marketing KPIs are built on trusted, well-modeled data.\n Translate ambiguous business questions from Marketing, Sales, and R\u0026D into clear requirements and scalable solutions, working autonomously without dedicated PM support and influencing decisions with data and sound reasoning.\n Lead complex, cross-team initiatives end to end: stand up new data pipelines and integrations quickly, manage risk across interdependent teams, and own the reliability and data quality of what ships.\n Serve as the senior technical lead for the team's hardest problems: raise the bar for engineering quality and code review, remove bottlenecks that slow multiple teams, and mentor engineers and analysts. \n Drive the strategic use of AI to solve ambiguous, high-impact problems: build AI-enabled data products and agents, with the integrations, retrieval pipelines, and evaluation practices that keep them reliable and trustworthy.\n Identify and automate manual workflows with AI across Marketing and GTM teams, taking ideas from concept to prototype to production and influencing org roadmaps.\n Ship high-quality Python and SQL, increasingly by directing agentic coding tools, while holding a high bar for reviewing and verifying AI-generated work before it reaches production.\n Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices.\n \n Minimum requirements for the role: \n \n 8+ years of experience in data engineering, analytics engineering, or AI engineering, including deep hands-on data engineering experience.\n Expert-level Python and SQL, with strong hands-on data modeling and DBT experience.\n Deep experience with data warehouse and lakehouse architectures, ETL/ELT, and the modern data stack, including hands-on Databricks (or similar, such as Snowflake or BigQuery).\n Experience building sema","salary_min":137445,"salary_max":231000,"location":"Chicago, IL","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","agents","fine-tuning","data-pipeline"],"apply_url":"https://www.samsara.com/company/careers/roles/8281490?gh_jid=8281490","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T14:42:51Z","expires_at":"2026-11-13T16:55:44.363812Z","created_at":"2026-10-14T15:19:50.593246Z","updated_at":"2026-10-14T16:55:44.366919Z","company_name":"Samsara","company_slug":"samsara","company_logo_url":"https://www.google.com/s2/favicons?domain=www.samsara.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d1f23f74-64a2-4091-ad96-0a63c47efea2"},{"id":"ebf01d34-f36c-4733-9129-c60c1618e687","company_id":"01b03876-5b01-47ab-82b9-696293e861b9","title":"Senior AI Data \u0026 Analytics Engineer II","slug":"senior-ai-data-analytics-engineer-ii-6a45f587","description":"Who we are \n Samsara (NYSE: IOT) is the pioneer of the Connected Operations™ Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. At Samsara, we are helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing — and we are excited to help digitally transform their operations at scale. \n Working at Samsara means you’ll help define the future of physical operations and be on a team that’s shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you’ll have the autonomy and support to make an impact as we build for the long term. \n About the role: \n Want to build the data foundation powering AI and self-serve analytics at scale? As part of Samsara's Marketing Data and Analytics (MDA) Engineering team, you will shape the technical roadmap alongside our BI and Data Science peers. You will architect core Databricks pipelines, Gold Layer tables, and semantic models, turning ambitious, ambiguous business challenges into high-value data products, autonomous agents, and cross-functional automation.\n This role is open to candidates residing in the US.\n You should apply if: \n \n You want to impact the industries that run our world: Your efforts will result in real-world impact – helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.\n You are the architect of your own career: If you put in the work, this role won’t be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment and master your craft in a hyper growth environment.\n You’re energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.\n You want to be with the best: At Samsara, we win together, celebrate together and support each other. You will be surrounded by a high-calibre team that will encourage you to do your best. \n \n In this role, you will: \n \n Partner on defining the strategy and multi-quarter roadmap for MDA's data and AI foundation.\n Architect and own Samsara's marketing data infrastructure: ETL/ELT pipelines, Databricks Gold Layer tables, and the core data models that power segmentation, targeting, automation, and analytics.\n Design the semantic layers, data dictionaries, and structured documentation that enable self-serve BI and conversational analytics, partnering with the BI team to ensure core sales and marketing KPIs are built on trusted, well-modeled data.\n Translate ambiguous business questions from Marketing, Sales, and R\u0026D into clear requirements and scalable solutions, working autonomously without dedicated PM support and influencing decisions with data and sound reasoning.\n Lead complex, cross-team initiatives end to end: stand up new data pipelines and integrations quickly, manage risk across interdependent teams, and own the reliability and data quality of what ships.\n Serve as the senior technical lead for the team's hardest problems: raise the bar for engineering quality and code review, remove bottlenecks that slow multiple teams, and mentor engineers and analysts. \n Drive the strategic use of AI to solve ambiguous, high-impact problems: build AI-enabled data products and agents, with the integrations, retrieval pipelines, and evaluation practices that keep them reliable and trustworthy.\n Identify and automate manual workflows with AI across Marketing and GTM teams, taking ideas from concept to prototype to production and influencing org roadmaps.\n Ship high-quality Python and SQL, increasingly by directing agentic coding tools, while holding a high bar for reviewing and verifying AI-generated work before it reaches production.\n Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices.\n \n Minimum requirements for the role: \n \n 8+ years of experience in data engineering, analytics engineering, or AI engineering, including deep hands-on data engineering experience.\n Expert-level Python and SQL, with strong hands-on data modeling and DBT experience.\n Deep experience with data warehouse and lakehouse architectures, ETL/ELT, and the modern data stack, including hands-on Databricks (or similar, such as Snowflake or BigQuery).\n Experience building sema","salary_min":137445,"salary_max":231000,"location":"Austin, TX","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","data-pipeline","llm","agents"],"apply_url":"https://www.samsara.com/company/careers/roles/8281489?gh_jid=8281489","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T14:42:13Z","expires_at":"2026-11-13T16:55:44.36902Z","created_at":"2026-10-14T15:19:50.665308Z","updated_at":"2026-10-14T16:55:44.37195Z","company_name":"Samsara","company_slug":"samsara","company_logo_url":"https://www.google.com/s2/favicons?domain=www.samsara.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ebf01d34-f36c-4733-9129-c60c1618e687"},{"id":"431ba833-ac4a-4ffc-b1eb-ea585214fc8c","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Mission Operations Product Manager, Group 5 Mission Autonomy","slug":"mission-operations-product-manager-group-5-mission-autonomy-96b19d8b","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n WHAT YOU’LL DO   \n \n Develop Mission Autonomy products that support the operation of Group 5 autonomous air vehicles \n Drive product development for the entire lifecycle from initial requirements development to fielding\n Define product roadmaps and timelines, guide cross-functional product decisions, and manage engineering risk\n Working closely with engineering leads and business development, evaluate trades to enable exploration of early designs\n Develop autonomy behavior and supporting product requirements. Collaborate with engineering teams to implement requirements\n Work collaboratively with cross-functional teams, including software engineers, hardware engineers, test engineers, and program managers, to define test requirements and ensure alignment between system capabilities and mission objectives in complex operational scenarios\n Plan, execute, and analyze mission autonomy tests in simulated environments that replicate real-world scenarios to validate system performance and identify areas for improvement, iterating rapidly to refine autonomy behaviors and supporting products\n Support business development teams by being the product voice at the table with customers through customer briefings/demonstrations/exercises\n Support business development in creating and maintain updated presentation templates of product capabilities that can be used as an 80% solution for customer briefs\n \n   \n REQUIRED QUALIFICATIONS   \n \n Operational experience as a Pilot or CSO/NFO of USAF, USN, USMC 4th or 5 th generation fighter/bomber aircraft or RPAs \n Self-starter with high ownership, mission-oriented mentality. Desire to see the product meet the need of the end customer\n Strong analytical skills, with the ability to interpret test data, diagnose system behaviors, and recommend actionable improvements\n Organized and detail-oriented, with strong verbal \u0026 written communication skills\n Proven track record of working collaboratively with multidisciplinary teams to achieve project milestones and deliverables\n Experience in leading system design and development from initial concept through delivery to customers\n Ability to leverage operational experience and technical knowledge to make educated decisions and leverage engineering trades in the absence of clear customer requirements\n Willingness to work flexible hours. Up to 25% travel as required for customer engagements and product development activities\n Active U.S. Top Secret security clearance and experience with Special Access Programs   \n \n PREFERRED QUALIFICATIONS   \n \n 5th generation aircraft pilot experience preferred \n Weapons school instructor, weapons school graduate, test pilot school graduate, Developmental or Operational Test experience\n Bachelor’s degree in STEM, or equivalent experience in a relevant field\n Direct experience in the development, testing, and deployment of mission autonomy systems in a defense or aerospace context\n Familiarity with agile software development practices \n Knowledge of current industry and military standards related to unmanned aerial systems and their operation within national airspace\n Familiarity with the principles of mission autonomy, unmanned systems, and the integration of multiple assets into cohesive operational frameworks\n US Salary Range\n $191,000 — $253,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:   \n  \n Benefits \n At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits . \n  \n \n Protecting Yourself from Recruitment Scams \n Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidat","salary_min":191000,"salary_max":253000,"location":"Washington, DC","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["computer-vision","cloud","payments","fine-tuning"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5265377007?gh_jid=5265377007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T13:10:03Z","expires_at":"2026-11-13T16:57:15.893852Z","created_at":"2026-10-14T15:21:01.028112Z","updated_at":"2026-10-14T16:57:15.896738Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/431ba833-ac4a-4ffc-b1eb-ea585214fc8c"},{"id":"8e20950b-3b3e-46d3-bbbc-c7144188aaa9","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Mission Operations Product Manager, Group 5 Mission Autonomy","slug":"mission-operations-product-manager-group-5-mission-autonomy-36628482","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n WHAT YOU’LL DO   \n \n Develop Mission Autonomy products that support the operation of Group 5 autonomous air vehicles \n Drive product development for the entire lifecycle from initial requirements development to fielding\n Define product roadmaps and timelines, guide cross-functional product decisions, and manage engineering risk\n Working closely with engineering leads and business development, evaluate trades to enable exploration of early designs\n Develop autonomy behavior and supporting product requirements. Collaborate with engineering teams to implement requirements\n Work collaboratively with cross-functional teams, including software engineers, hardware engineers, test engineers, and program managers, to define test requirements and ensure alignment between system capabilities and mission objectives in complex operational scenarios\n Plan, execute, and analyze mission autonomy tests in simulated environments that replicate real-world scenarios to validate system performance and identify areas for improvement, iterating rapidly to refine autonomy behaviors and supporting products\n Support business development teams by being the product voice at the table with customers through customer briefings/demonstrations/exercises\n Support business development in creating and maintain updated presentation templates of product capabilities that can be used as an 80% solution for customer briefs\n \n   \n REQUIRED QUALIFICATIONS   \n \n Operational experience as a Pilot or CSO/NFO of USAF, USN, USMC 4th or 5 th generation fighter/bomber aircraft or RPAs \n Self-starter with high ownership, mission-oriented mentality. Desire to see the product meet the need of the end customer\n Strong analytical skills, with the ability to interpret test data, diagnose system behaviors, and recommend actionable improvements\n Organized and detail-oriented, with strong verbal \u0026 written communication skills\n Proven track record of working collaboratively with multidisciplinary teams to achieve project milestones and deliverables\n Experience in leading system design and development from initial concept through delivery to customers\n Ability to leverage operational experience and technical knowledge to make educated decisions and leverage engineering trades in the absence of clear customer requirements\n Willingness to work flexible hours. Up to 25% travel as required for customer engagements and product development activities\n Active U.S. Top Secret security clearance and experience with Special Access Programs   \n \n PREFERRED QUALIFICATIONS   \n \n 5th generation aircraft pilot experience preferred \n Weapons school instructor, weapons school graduate, test pilot school graduate, Developmental or Operational Test experience\n Bachelor’s degree in STEM, or equivalent experience in a relevant field\n Direct experience in the development, testing, and deployment of mission autonomy systems in a defense or aerospace context\n Familiarity with agile software development practices \n Knowledge of current industry and military standards related to unmanned aerial systems and their operation within national airspace\n Familiarity with the principles of mission autonomy, unmanned systems, and the integration of multiple assets into cohesive operational frameworks\n US Salary Range\n $191,000 — $253,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:   \n  \n Benefits \n At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits . \n  \n \n Protecting Yourself from Recruitment Scams \n Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidat","salary_min":191000,"salary_max":253000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["payments","computer-vision","fine-tuning","cloud"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5247776007?gh_jid=5247776007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T13:10:01Z","expires_at":"2026-11-13T16:57:15.88163Z","created_at":"2026-10-14T15:21:01.032558Z","updated_at":"2026-10-14T16:57:15.885257Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8e20950b-3b3e-46d3-bbbc-c7144188aaa9"},{"id":"7f073f29-8892-4033-ab21-b488ea264615","company_id":"d90e5b83-7941-4bd8-aace-5327c84c09c6","title":"Software Engineer, Applied Machine Learning","slug":"software-engineer-applied-machine-learning-b6a1a5f9","description":"fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.\n\nAs generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.\n\n\n\n\nABOUT THIS ROLE:\n\nWe are seeking a hands-on, production-focused Applied Machine Learning Engineer to take technical ownership of the model layer powering our next-generation generative media platform. In this role, you will bridge the gap between cutting-edge generative research and scalable, consumer-facing products.\n\nYou will split your time between extending SOTA open-source models with additional capabilities and helping maintain our fleet of generative model APIs.\nWhat you’ll do:\n\n - Novel Model Pipelines: Work with our post-training team to extend SOTA image, video, audio, and 3D models with additional capabilities and modalities. Develop novel approaches to model conditioning, generation, and editing, including both training-free methods and model fine-tuning.\n\n - Fine-Tuning: Leverage our massive GPU fleet to fine-tune generative models for novel capabilities. Build and maintain fine-tuning APIs that allow customers to customize models for their specific needs.\n\n - Architecture \u0026 Abstraction: Identify common patterns across the models we serve and develop reusable components, abstractions, and building blocks that accelerate the development of new model capabilities and inference pipelines.\n\n - Inference Optimization: Work hand-in-hand with our ML Performance \u0026 Optimization team to apply state-of-the-art inference techniques and best practices, ensuring models run efficiently with low latency, high throughput, and optimal GPU utilization.\n\n - Production Deployment: Build, deploy, and maintain scalable, reliable generative model APIs. Anticipate and resolve production challenges to ensure our models serve customers reliably at scale.\n\n - Customer Collaboration: Work directly with customers, including some of the world's largest e-commerce retailers and film and TV production studios, to develop novel solutions to their generative media needs.\n\nQualifications/Nice-to-haves:\n\n - Experience: 3+ years of professional experience as an Applied ML Engineer, with at least 1–2 years focused on generative media or computer vision.\n\n - Core Frameworks: Expert-level proficiency in Python and PyTorch.\n\n - Generative Media: Deep practical understanding of diffusion and flow-based generative models.\n\n - Open-Source Tooling: Hands-on experience working with open-weight model ecosystems, including Hugging Face, Diffusers, and related tooling.\n\n - Engineering Rigor: Ability to anticipate and solve challenges that arise when deploying ML models to production. Strong engineering judgment in designing systems that are scalable, reliable, secure, safe, and performant.\n\n - Training-Free Model Extensions: Experience designing and implementing training-free extensions to image, video, audio, or 3D generative models, such as novel conditioning methods, inference-time modifications, or new model capabilities.\n\n - Model Post-Training: Experience developing and executing custom post-training or fine-tuning approaches to extend generative models with additional capabilities.\n\n - Startup Experience: Track record of working in fast-paced startup environments or digital media and entertainment industries.\n\n - Ability to Ship: Demonstrated ability to independently take ambitious ML ideas from concept to production.\n\nWhat we offer at fal:\n\n - Interesting and challenging work\n\n - A lot of learning and growth opportunities\n\n - Health, dental, and vision insurance (US)\n\n - Regular team events and offsites\n\n\nCOMPENSATION\n\n - $180,000 - $230,000 + equity + comprehensive benefits package\n\n \n\n\n\nU.S. EQUAL EMPLOYMENT OPPORTUNITY INFORMATION:\n\nfal provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other classification protected by applicable law.","salary_min":180000,"salary_max":230000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["computer-vision","pytorch","fine-tuning","machine-learning"],"apply_url":"https://jobs.ashbyhq.com/fal-ai/c351a0de-1826-4968-a023-a5243aba2ec6/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T21:14:59.835Z","expires_at":"2026-11-13T16:57:32.416891Z","created_at":"2026-10-14T15:36:11.028291Z","updated_at":"2026-10-14T16:57:32.420794Z","company_name":"Fal","company_slug":"fal","company_logo_url":"https://www.google.com/s2/favicons?domain=fal.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7f073f29-8892-4033-ab21-b488ea264615"},{"id":"bfd991d3-7f62-425c-bcfb-f01c579b3fb5","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Data and AI Platform","slug":"software-engineer-data-and-ai-platform-a47789b0","description":"DoorDash is building the world's most reliable on-demand, logistics engine for delivery! We're looking for talented engineers to help us develop a 24x7, global infrastructure system that powers DoorDash's three-sided marketplace of consumers, merchants, and dashers.\n About the Team \n Data Platform’s Data User Experience Engineering builds the products every DoorDash employee uses to answer questions with data. That portfolio includes our AI first reporting and authoring surface,Conversational analytics with AI, Semantic and Knowledge layer along with MCP and skills layer plus the full stack infrastructure that lets users and agents reach all of it. Our users span analysts, engineers, operators, and executives across DoorDash, Wolt, and Deliveroo, on a warehouse and OLAP footprint that includes Snowflake,Spark and Clickhouse. The team is a mix of engineers who have built AI \u0026 Data platforms and products at scale.\n About the Role \n You will own the full path from a question to an answer an operator can put in a deck without checking it twice. The BI reporting surfaces are converging, and deciding what that convergence looks like while someone is authoring, building a report, writing a query, asking why a number moved, or asking for the chart they were about to build by hand.\n You’re excited about this opportunity because you will… \n \n Ship agentic analytics features to thousands of users  where a wrong number is a real business decision made badly, and accuracy is the product\n Build a data platform for authoring, dashboard runtime, drill-down and cross-filter semantics, scheduling and alerting, content certification and permissions, and the embedding path that puts the same components in front of external users\n Streaming large result sets to the browser, rendering dozens of interdependent tiles.\n Build the AI experience inside that platform: agent-assisted query authoring, generated charts and widgets held to the same governance as hand-built ones\n Evolve the semantic layer  metric and dimension modeling, definition governance, lineage  and expose metadata in forms a model can actually reason over\n Own the query and adaptive caching path: planning and federation across different query engines and invalidation correctness, and safe execution under agent traffic that bursts and fans out\n Develop AI-enabled services and workflows, including self-serve tooling for creating, evaluating, and improving AI assistants\n Build and operate data discovery and metadata platforms that help users find the right datasets, dashboards, and metrics quickly and confidently\n \n We're excited about you because you have… \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 fundamentals, especially in Go,Python and the ability to own a service in production \n Shipped an LLM-powered feature to real users, hands-on with retrieval and grounding and built or meaningfully extended an eval system\n Built meaningful pieces of data platform products- semantic layers, data discovery, reporting applications, caching systems and data applications\n Familiarity with a cloud-based environment such as AWS and with Kubernetes in production\n Experience with MCP, agent frameworks, or tool-calling architectures in production\n Experience with traditional and modern reporting or analytics tooling, data governance, controls and multi-tenant data platforms\n Prompt and context optimization at scale -  caching, token budgeting, cost/latency tuning\n Full stack experience is a plus\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["fine-tuning","llm","cloud","healthcare","computer-graphics","agents"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8192187","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T20:21:21Z","expires_at":"2026-11-13T17:03:28.751716Z","created_at":"2026-10-14T15:25:31.393614Z","updated_at":"2026-10-14T17:03:28.754637Z","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/bfd991d3-7f62-425c-bcfb-f01c579b3fb5"},{"id":"b34f4013-d975-493b-8a19-92a0a531c511","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Software Engineer, Machine Learning","slug":"staff-software-engineer-machine-learning-c84454fb","description":"About the Team \n DoorDash is building the world's most reliable on-demand, logistics engine for delivery. We are continuing to grow rapidly and expanding our Engineering offices globally! We are looking for Software Engineers, Machine Learning to build and maintain a large scale 24x7 global infrastructure system that powers DoorDash's 3-sided marketplace of Consumers, Merchants and Dashers.\n About the Role \n As a Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams.\n This role is hybrid with some in-office time expected and will report to an Engineering Manager. \n You're excited about this opportunity because you will... \n \n Develop advanced machine learning models to improve ads efficiency and quality.\n Design and build optimization algorithms for budget pacing and automated bidding to achieve various advertising goals. \n Establish a data-driven framework to understand how the bid density and market competitiveness would affect advertising value and platform revenue. \n Develop new data solutions (eg. embeddings and consumer profiles) to target the relevant audience.\n Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement.\n Build and extend the current data/ML infrastructure to empower Ads data applications including data analysis, ML modeling, and experimentation.\n Scale our systems and services to fuel the growth of our business.\n \n We're excited about you because you have... \n \n M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent.\n 8+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals.\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 Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL.\n Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, LightGBM, scikit-learn, Spark ML, or related.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $203,500 — $299,300 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","cloud","healthcare","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8092262","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:49:36Z","expires_at":"2026-11-13T17:03:28.820143Z","created_at":"2026-10-14T15:25:31.461436Z","updated_at":"2026-10-14T17:03:28.823202Z","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/b34f4013-d975-493b-8a19-92a0a531c511"},{"id":"2bdabd0b-9451-4388-b502-2ba7d839c43f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, Drive","slug":"senior-machine-learning-engineer-drive-202e134e","description":"About the Team\n DoorDash Drive powers deliveries placed through merchants' own channels—including their websites, mobile apps, and phone orders—using DoorDash's logistics network. The Drive Machine Learning team builds the prediction and intelligence systems that power this business, including delivery and pickup time estimation, merchant prep-time prediction, order release optimization, logistics decision-making, and AI-powered delivery quality signals. Drive presents a unique machine learning challenge. Every merchant has different operational workflows, preparation patterns, and customer expectations, requiring models that generalize across millions of deliveries while adapting to highly diverse merchant behavior. Our team has significant opportunities to improve prediction accuracy, optimize logistics decisions, and build AI-native experiences that directly improve merchant, consumer, and dasher outcomes.\n About the Role\n As a Machine Learning Engineer on the Drive team, you'll own machine learning systems end-to-end—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration. Your work will span several high-impact problem areas:\n \n Build next-generation machine learning models for delivery ETA, pickup ETA, merchant prep-time estimation, and order release prediction that improve reliability for merchants and consumers.\n Develop deep learning models that leverage large-scale spatiotemporal, marketplace, and behavioral signals to improve prediction accuracy.\n Apply reinforcement learning and optimization techniques to improve logistics decision-making, assignment strategies, and marketplace efficiency.\n Build AI-native product experiences using large language models (LLMs) and vision-language models (VLMs). For example, transform pickup photos, item verification flows, receipts, and drop-off images into structured quality signals that help verify orders, prevent delivery defects, and improve issue resolution.\n Design and run rigorous online experiments, production monitoring, and model iteration to continuously improve performance.\n Partner closely with software engineers, product managers, data scientists, and platform teams to bring new machine learning capabilities into production at scale.\n You'll have the opportunity to work across traditional machine learning, deep learning, reinforcement learning, optimization, and multimodal AI while solving some of the most challenging logistics problems at DoorDash.\n \n You're excited about this opportunity because... \n \n You enjoy solving large-scale machine learning problems that directly impact millions of deliveries.\n You have a strong sense of ownership and enjoy taking models from research through production.\n You're comfortable working in ambiguous environments where experimentation and iteration drive product decisions.\n You care about both model quality and production reliability.\n You're excited to work across a diverse set of ML techniques—from neural networks and optimization to multimodal AI.\n You collaborate well across engineering, product, and data science teams.\n \n We’re excited about you because… \n \n 5+ years of industry experience building and shipping production machine learning systems with measurable business impact (Bachelor's, Master's, or PhD).\n Strong experience developing production machine learning models using modern deep learning frameworks such as PyTorch and distributed data processing technologies such as Spark and Airflow.\n Experience building, deploying, monitoring, and maintaining production ML systems end-to-end.\n Strong software engineering skills in Python and experience with modern ML infrastructure and tooling.\n Deep expertise in at least one of the following areas:\n \n Deep Learning\n Reinforcement Learning\n Optimization / Operations Research\n Large Language Models (LLMs) or Vision-Language Models (VLMs)\n \n Experience applying machine learning to estimation, ranking, prediction, optimization, or decision-making problems at production scale.\n Hands-on experience with LLMs or VLMs is a strong plus.\n Experience in logistics, marketplaces, or delivery platforms is helpful but not required.\n Proficiency using AI-assisted development tools (e.g. Claude Code, Codex, Cursor) throughout the software development lifecycle.\n You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.\n Compensation \n The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions.  Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about yo","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","deep-learning","reinforcement-learning","cloud","llm","generative-ai","healthcare","pytorch"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8221807","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:42:26Z","expires_at":"2026-11-13T17:03:28.706911Z","created_at":"2026-10-14T15:25:31.350903Z","updated_at":"2026-10-14T17:03:28.710264Z","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/2bdabd0b-9451-4388-b502-2ba7d839c43f"},{"id":"78fae61b-8780-438a-b75a-b7c5778b3828","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Software Engineer, Machine Learning","slug":"senior-software-engineer-machine-learning-851e39b8","description":"About the Role \n As a Senior Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams. We expect Senior Engineers to be able to own projects end-to-end and mentor their junior and mid level peers.\n This role is hybrid with some in-office time expected and will report to an Engineering Manager. \n You're excited about this opportunity because you will... \n \n Develop advanced machine learning models to improve core parts of your teams scope.\n Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement.\n Work on creating novel ML \u0026 AI solutions that push both our business and the industry forward.\n Scale our systems and services to fuel the growth of our business.\n Completely disrupt logistics by tackling bleeding-edge, technical problems\n \n We're excited about you because you have... \n \n M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent.\n 5+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals.\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 Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL.\n Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, lightgbm, scikit-learn, Spark ML, or related.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $167,800 — $246,800 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.\n Our Commitment to Diversity and Inclusion\n We’re committed to growing and empowering a more inclusive","salary_min":167800,"salary_max":246800,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","healthcare","pytorch","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8143205","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:38:25Z","expires_at":"2026-11-13T17:03:28.716694Z","created_at":"2026-10-14T15:25:31.359543Z","updated_at":"2026-10-14T17:03:28.719518Z","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/78fae61b-8780-438a-b75a-b7c5778b3828"},{"id":"9b894a7b-e6ce-485c-b095-970a736af6dc","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, New Verticals Agentic Foundations","slug":"senior-machine-learning-engineer-new-verticals-agentic-foundations-a3f3a5d5","description":"About the Team\n Agentic Foundations, part of New Verticals ML, builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash, and proves it through flagship agent experiences such as the Ask grocery assistant. We operate as a closed loop: new models, evals, and representations make our agents better, and real-world agent usage surfaces the gaps that drive our next foundations work. \n About the Role\n We’re looking for a Senior Machine Learning Engineer to join Agentic Foundations with a primary focus on Vertical Agent Development. You will own the continuous improvement of our flagship agents, starting with the Ask Assistant, using an eval-driven loop to make them faster, more cost-efficient, and more reliable for millions of customers. You will also help us bring this approach to new agentic experiences for Merchants and Dashers, turning promising ideas into production-ready agents.\n This is a chance to work at the intersection of LLMs, agents, post-training, and evals on problems that ship. You will partner closely with engineers working on agentic memory, agent-compatible product representations, and post-training of small language models (SLMs), so that what we learn from production agents feeds directly back into our foundations, and what we build there makes our agents better.\n You will report into the engineering lead on our New Verticals AI/ML team. We expect this role to be hybrid with some time in-office and some time remote.\n You’re excited about this opportunity because you will…\n \n Raise the quality of our Consumer agents: build and refine our eval harness-optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context, and measure the impact on real customer tasks.\n Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can replace or augment larger LLMs.\n Launch new agents: take agentic opportunities for Merchants and Dashers from early exploration to production.\n Turn foundations into product impact: work with teammates on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation, and bring those capabilities into production agents.\n Shape the roadmap: partner with engineering, product, and business leaders to define an ML-driven strategy for our fast-growing grocery and retail delivery business.\n \n We’re excited about you because you have…\n \n 3+ years of industry ML experience, including hands-on work building and shipping LLM-based agents (tool use, context management, prompting, guardrails) and improving them with evals and data\n Experience with post-training or fine-tuning of open-weights models (e.g., SFT, preference optimization, or RL), ideally including small language models, and sound judgment on quality, latency, and cost trade-offs\n Strong foundation in NLP and machine learning, with proficiency in Python and frameworks such as PyTorch or TensorFlow\n M.S. or PhD in Computer Science, Statistics, Math, or another quantitative field, or equivalent practical experience, plus a collaborative, growth-minded approach and a drive for measurable impact\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United","salary_min":167800,"salary_max":246800,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","llm","fine-tuning","agents","cloud","pytorch","nlp","healthcare"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8279145","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:38:05Z","expires_at":"2026-11-13T17:03:28.711787Z","created_at":"2026-10-14T15:25:31.355292Z","updated_at":"2026-10-14T17:03:28.714838Z","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/9b894a7b-e6ce-485c-b095-970a736af6dc"},{"id":"634e56a9-b38d-474b-847e-3dc417b38339","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Personalization","slug":"staff-machine-learning-engineer-personalization-d2d9e9a7","description":"About the Team \n Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop modern growth and personalization models that power DoorDash's growing retail and grocery business.\n About the Role \n We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning,  experience with solving end-user problems, and collaborate well with multi-disciplinary teams.\n You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).\n You’re excited about this opportunity because you will… \n \n Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space\n Partner with engineering and product leaders to help shape the product roadmap applying ML\n Mentor junior team members, and lead cross functional pods to create collective impact\n \n You can find out more on our ML blog here \n We’re excited about you because you have… \n \n 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. \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 M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field\n Expertise in applied ML for  Causal Inference and Recommendation Systems  -  both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms \u0026 LLMs is a plus.  \n Machine learning background in Python; experience with PyTorch or TensorFlow preferred.\n Ability to communicate technical details to nontechnical stakeholders\n You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down\n Desire for impact with a growth-minded and collaborative mindset\n \n \n Data Retention, Privacy \u0026 Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page . \n Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only\n We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.\n The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey \n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","cloud","deep-learning","fine-tuning","healthcare","pytorch","tensorflow","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8225006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:31:26Z","expires_at":"2026-11-13T17:03:28.796671Z","created_at":"2026-10-14T15:25:31.438732Z","updated_at":"2026-10-14T17:03:28.799759Z","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/634e56a9-b38d-474b-847e-3dc417b38339"},{"id":"501a4fae-853d-480b-9dbb-39daa44f8047","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Fulfillment Planning","slug":"staff-machine-learning-engineer-fulfillment-planning-53570b60","description":"About the Team \n The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability.  Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation.\n The team works on some of DoorDash’s most important logistics systems, including:\n \n The core assignment engine that matches deliveries with Dashers in real time.\n Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines.\n Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering.\n ML models and optimization algorithms that shape demand, improve service quality, and reduce cost.\n Tier-0 logistics services that require high reliability, low latency, and strong operational discipline.\n \n The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash.\n About the Role \n We’re looking for a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that drive real-time decisioning across DoorDash’s fulfillment ecosystem.\n You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines.\n This is a high-impact individual contributor role for someone who enjoys building 0→1 ML systems, operating at Staff-level scope, and influencing technical direction across multiple teams. You will define architectures, set modeling and deployment standards, mentor other engineers, and help shape how DoorDash applies machine learning to logistics at scale.\n You’re excited about this opportunity because you will… \n \n Own and build foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.\n Work on challenging, real-world machine learning problems , including real-time assignment, routing, and fulfillment estimation.\n Lead 0→1 ML initiatives , defining how machine learning and optimization are applied across fulfillment products.\n Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.\n Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.\n Establish best practices for model development, deployment, monitoring, retraining, and governance.\n Define and lead DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics\n Mentor other engineers and raise the technical bar for logistics ML across the organization.\n \n We’re excited about you because… \n \n You have 8+ years of industry experience building and deploying production-scale machine learning systems.\n You have strong machine learning fundamentals and know how to apply them to large-scale production systems.\n You are fluent in Python\n You have hands-on experience with modern ML frameworks, especially deep learning frameworks.\n You have designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.\n You can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.\n You communicate clearly with both technical and non-technical audiences.\n You are comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.\n You have built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.\n You have experience with knowledge distillation from large teacher models into efficient production models.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares ","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","healthcare","cloud","generative-ai","llm","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8224336","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:30:37Z","expires_at":"2026-11-13T17:03:28.79242Z","created_at":"2026-10-14T15:25:31.434336Z","updated_at":"2026-10-14T17:03:28.795093Z","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/501a4fae-853d-480b-9dbb-39daa44f8047"},{"id":"8bf0267f-9f49-4c14-b3ad-dfe54d410a18","company_id":"1d9e5b7c-1619-4467-a0f1-cee763869454","title":"AI Software Engineer","slug":"ai-software-engineer-c9bf9f69","description":"At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nSnowflake is building the most powerful AI Data Cloud in the world — and Canada is a critical part of that mission. Our AI/ML engineering teams are delivering the intelligence layer that powers Cortex AI, large language model inference, AI functions, intelligent search, and the next generation of AI-powered experiences on the Snowflake platform.\n\nWe're looking for a Software Engineer — AI to join our growing Toronto engineering team. You'll design, build, and ship AI-powered systems and services that are used by thousands of organizations worldwide. If you're passionate about building production AI infrastructure at scale and want to work alongside world-class engineers on problems that matter, this is the role.\n\n\nAS A SOFTWARE ENGINEER — AI, YOU WILL:\n\n - Design, develop, and ship AI-powered features and services — working across model inference, AI functions, retrieval systems, and intelligent application experiences on the Snowflake platform\n\n - Build production-quality infrastructure for AI workloads — including model serving, embedding pipelines, retrieval-augmented generation (RAG), and AI-driven automation\n\n - Write clean, performant, and well-tested code in languages like Python, Java, C++, or Go — contributing to systems that operate at enterprise scale across Snowflake's multi-cloud infrastructure\n\n - Collaborate with ML researchers, senior engineers, and product managers to translate AI research and product requirements into robust, scalable engineering solutions\n\n - Contribute to system design — participate in design reviews, propose architectural improvements, and help shape the technical direction of AI services\n\n - Own your work end-to-end — from design and implementation through testing, deployment, monitoring, and operational health\n\n - Stay current on AI/ML advancements — evaluate new techniques, models, and tools and assess their applicability to Snowflake's platform and customer needs\n\n - Contribute to a strong engineering culture — through code reviews, documentation, knowledge sharing, and continuous improvement of team practices\n\n\nOUR IDEAL SOFTWARE ENGINEER — AI WILL HAVE:\n\nRequired:\n\n - 3+ years of software engineering experience building production systems, with a strong foundation in computer science fundamentals (data structures, algorithms, distributed systems)\n\n - Experience with AI/ML systems — you've worked on model serving, inference pipelines, LLM integration, embedding systems, or AI-powered application features in a production environment\n\n - Proficiency in one or more of: Python, Java, C++, or Go\n\n - Experience building distributed systems or cloud-native services — you understand the challenges of scale, reliability, and performance in a multi-cloud environment\n\n - Strong problem-solving skills and the ability to break down ambiguous problems into clear engineering tasks\n\n - Collaborative mindset — you work effectively with engineers, researchers, and product managers and communicate your ideas clearly\n\n - Ownership mentality — you take responsibility for your work from design through production and care about quality, reliability, and operational excellence\n\nPreferred:\n\n - Experience with LLM inference, fine-tuning, RAG architectures, or vector search at production scale\n\n - Familiarity with ML frameworks such as PyTorch, TensorFlow, or similar\n\n - Experience with cloud infrastructure (AWS, Azure, or GCP) and containerization (Docker, Kubernetes)\n\n - Background in data platforms, data warehousing, or SQL-based systems\n\n - Experience with CI/CD pipelines, automated testing, and production monitoring/observability\n\n - Contributions to open-source AI/ML projects or published research in AI/ML\n\n\nSNOWFLAKE VALUES WE'RE LOOKING FOR:\n\n - Get It Done — You ship working software. You move fast, iterate, and deliver results — not just prototypes.\n\n - Own It — You take end-to-end responsibility for your code, your systems, and your outcomes. If something breaks, you fix it.\n\n - Make Each Other the Best — You contribute to a culture of learning through thoughtful code reviews, knowledge sharing, and helping teammates grow.\n\n - Think Big — You don't just solve today's problem. You think about how AI at Snowflake should work at 10x scale and buil","salary_min":170000,"salary_max":500000,"location":"Toronto, Canada","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["rag","mlops","pytorch","fine-tuning","cloud","tensorflow","agents","llm"],"apply_url":"https://jobs.ashbyhq.com/snowflake/628f36e0-8554-4d5f-8d5e-a017a761ca54/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T00:00:58.144Z","expires_at":"2026-11-13T16:59:18.383644Z","created_at":"2026-10-14T15:22:50.102644Z","updated_at":"2026-10-14T16:59:18.386616Z","company_name":"Snowflake","company_slug":"snowflake","company_logo_url":"https://www.google.com/s2/favicons?domain=snowflake.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8bf0267f-9f49-4c14-b3ad-dfe54d410a18"},{"id":"fad93271-34c1-404a-b2f3-8bc475aaf06a","company_id":"7f631da3-2c29-4575-a413-a9ed92f8af53","title":"Senior AI Engineer, Agentic Systems","slug":"senior-ai-engineer-agentic-systems-1274f235","description":"About the team: \n Rubrik secures and accelerates the world’s AI transformation. As AI agents reshape the enterprise, Rubrik’s Enterprise AI Incubation team proves it on Rubrik itself. We are building a thinking enterprise: applications that don't just record the business but help run it. They learn from our data, act through our own agents, and improve with every decision. Fully backed by executive leadership, this team drives meaningful, large-scale enterprise transformation.\n About the role:\n We want senior engineers who have already shipped agents that real people rely on, and who are ready to do it again at higher stakes. You will drop into the real work, redesign it around agents that carry it end to end, and ship it as the new way that part of Rubrik operates.You’ll own your agent systems end to end: understanding the workflow, designing the architecture, writing the code, deploying, and running it in production. Rather than simply querying an API, you engineer robust, production-grade infrastructure around model endpoints, integrating custom tooling, context pipelines, automated evaluations, and defensive guardrails to ensure resilience against unstructured real-world data.\n You also decide whether a problem needs an agent, a simpler automation, or nothing at all, and you have explicit authority to stop or re-scope work that won’t deliver impact. You thrive in ambiguous environments with a low-ego mindset, leveraging AI coding tools as core collaborative partners in your engineering workflow.\n What you’ll do:\n \n Lead product discovery alongside Product Managers and domain experts to understand real workflows, map them out, and define clear problems, baselines, and success criteria as the foundation for building agentic solutions.\n Design, build, test, and deploy production agents, automations, and internal tools using coding agents, LLM APIs, and agent frameworks, integrated with Rubrik’s internal agent platform and business systems.\n Turn complex processes into multi-agent workflows that carry real work and make decisions: orchestrators that qualify and routes deals, workers that move orders through approval and fulfillment under business rules, agents that monitor signals and recommend actions for customer health, or coordinated agents that handle hire-to-retire steps across HR, IT, and managers with shared state and clear handoffs.\n Engineer agents for autonomy with reliability, including tool and orchestration flows, context management, caching, retries, and failure handling.\n Leverage full-stack engineering across backend services, APIs, data models, interactive screens, approval UIs, and chat interfaces as the complete medium to deliver high-ownership, user-facing agent systems.\n Apply solid software engineering practices to AI systems, treating prompts, skills, configs, and specs as versioned artifacts, with code review, tests, CI or CD, and staged rollouts.\n Implement evaluation, guardrails, and quality control: derive evals from workflow data, establish quality gates and guardrails, integrate human-in-the-loop approvals where needed, and monitor production runs to refine agents over time.\n Use measured results to decide how to evolve or retire agents, and turn successful systems into patterns and libraries that can be reused.\n \n Experience you’ll need:\n \n Around 7 or more years of professional software engineering experience in production systems, and around 2 or more years of professional, hands-on experience building AI agents and agent powered production applications with measurable outcomes.\n Strong full stack engineering background, with a track record of designing, building, and owning services end to end, including backend APIs and integrations, data models, and user facing interfaces such as approval UIs, dashboards, or chat surfaces.\n A builder mindset: comfortable defining baselines and metrics, reporting results in business terms, working in a fast moving environment, and closing the loop by building, demoing, learning, and building again. If you like seeing improvements in hours saved, errors avoided, and teams unblocked, you will have plenty of chances to measure that here.\n \n Software engineering skills and best practices:\n \n System design: break down complex problems, design APIs and data models, reason about latency, throughput, and failure modes, and choose simple, robust architectures.\n Algorithms, data structures, access control permissions, and data provenance.\n Spec driven development: turn vague requirements into clear problem statements, write or refine design docs, and use a Spec → Plan → Task → Code→ Converge flow so work is traceable from design to code, including when you use AI coding agents.\n Execution discipline: use version control, code review, automated tests, CI or CD, staged rollouts and rollbacks, logging, metrics, and tracing, and improve systems based on issues and signals from production.\n \n AI engineering skills and best practices","salary_min":188500,"salary_max":282700,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","cloud","api-design","agents","fine-tuning"],"apply_url":"https://www.rubrik.com/company/careers/departments/job.8274601?gh_jid=8274601","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T17:15:42Z","expires_at":"2026-11-13T17:03:40.183779Z","created_at":"2026-10-14T15:25:42.354845Z","updated_at":"2026-10-14T17:03:40.186843Z","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/fad93271-34c1-404a-b2f3-8bc475aaf06a"},{"id":"7700aa2a-0736-4261-86f4-43e527ee6f2c","company_id":"b4787255-dacd-444b-8e44-bb9971ec1f36","title":"Senior Director, Product Management, Agentic Platform","slug":"senior-director-product-management-agentic-platform-368b55e9","description":"ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.\n \n ZoomInfo has rebuilt itself around AI, and every agentic experience we ship runs on one shared platform — Copilot chat, agent teams, bots, GTM Studio, MarketingOS, and products that don't exist yet. As Senior Director of Product Management for the Agentic Platform, you'll be the right hand to our VP of Agents \u0026 Orchestration, owning the core primitives every agent depends on and the forward-looking R\u0026D that keeps our agents ahead of what off-the-shelf models and frameworks can do. The best practices for agentic platforms are being written right now, and you'll help write them.\n What You'll Do \n \n You will set the multi-year Agentic Platform strategy and turn it into a roadmap that balances reliability, speed for product teams, and bets on the frontier.\n You will own core platform primitives — multi-agent orchestration, planning and execution runtimes, memory and context management, tool and MCP infrastructure, guardrails, and observability — and define the abstractions that let product teams compose new agents quickly.\n You will lead the product side of custom model training and fine-tuning on ZoomInfo's proprietary data, deciding where custom models create lasting advantage versus frontier models.\n You will build the decisioning-model layer that lets agents choose the right action, partnering with data science to turn ZoomInfo's signals into models agents invoke as first-class capabilities.\n You will run a structured orchestration R\u0026D program that tracks emerging research and moves the winners into production.\n You will identify and ship net-new platform capabilities that unlock new product surfaces and revenue, taking ideas from whiteboard sketch to production primitive.\n You will partner with engineering to make agent infrastructure production-grade — measurable on execution at scale, latency, cost per task, security, and compliance.\n You will build and grow a team of senior and principal PMs, hiring for technical depth and ownership and developing leaders who run their own areas with the same bar you hold.\n \n What You Bring \n Must-Have: \n \n At least 10 years building technical products, including at least 3 years leading teams of PMs or running a company\n Deep, hands-on experience with production agentic systems — agents that plan, invoke tools, and take real actions, not just chat interfaces\n A track record of taking an AI or agentic product from 0→1, either as a founder or as the product leader who owned it end to end\n Fluency across the modern agentic stack, including orchestration frameworks (LangGraph, CrewAI, or custom), MCP and tool-calling patterns, RAG and memory architectures, and evals and observability\n Enough technical depth to review architecture docs, read agent traces, and prototype in code yourself\n \n Preferred: \n \n Working knowledge of model development, including fine-tuning, post-training, evaluation design, and the economics of custom versus frontier models\n Experience building platform products where engineering and product teams are your customers\n Experience shipping decisioning, ranking, or recommendation models into production\n Experience in B2B, GTM, or data platform businesses\n \n  \n  \n #LI-Remote\n #LI-VC1\n Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.\n In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here .\n Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.\n $192,920 — $303,160 USD \n About us:  \n ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.\n ZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.\n ZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available here .\n ZoomInfo is proud to be an equal opportunity employer, hiring based","salary_min":192920,"salary_max":303160,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["rag","code-generation","fine-tuning","agents"],"apply_url":"https://www.zoominfo.com/careers?gh_jid=8876309002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T17:48:12Z","expires_at":"2026-11-13T17:03:21.855103Z","created_at":"2026-10-14T15:25:25.418534Z","updated_at":"2026-10-14T17:03:21.859287Z","company_name":"ZoomInfo","company_slug":"zoominfo","company_logo_url":"https://www.google.com/s2/favicons?domain=zoominfo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7700aa2a-0736-4261-86f4-43e527ee6f2c"},{"id":"6146aded-8614-4fdf-a457-cef73b78ddbe","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Fullstack Software Engineer, Safeguards Engineering","slug":"staff-fullstack-software-engineer-safeguards-engineering-c0ee19d3","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n Note: this job post is for Fullstack Software Engineers only. If you are a Backend Engineer, please see our Backend Software Engineer post  here .  \n About the role \n We are looking for fullstack software engineers to help build safety and oversight mechanisms for our AI systems. As a software engineer on the Safeguards team, you will work to monitor models, prevent misuse, and ensure user well-being. This role will focus on building systems to detect unwanted model behaviors and prevent disallowed use of models. You will apply your technical skills to uphold our principles of safety, transparency, and oversight while enforcing our terms of service and acceptable use policies.\n We have multiple teams that are currently hiring within Safeguards. Team placement occurs after the interview process, taking into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers where they'll have the greatest impact and growth potential.  \n Responsibilities: \n \n Design and build the internal review tools analysts use to investigate abuse — dashboards, case queues, and evidence viewers that make high-volume, high-stakes decisions fast and accurate.\n Build the interfaces where humans supervise agents: streaming output, interruption and approval flows, and clear provenance for every action an agent takes.\n Surface abuse patterns to our research teams through the visualizations and exploration tools that make emerging patterns legible.\n Own the frontend architecture for tools handling sensitive data at scale — performance under large datasets, correctness under real-time updates, and access controls enforced end to end.\n \n You may be a good fit if you have: \n \n A Bachelor’s degree in Computer Science, Software Engineering or comparable experience\n Proficiency in TypeScript and modern React; comfortable in Python for backend work\n The ability to work across the stack\n Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders\n \n Strong candidates may also: \n \n 8+ years of experience in a software engineering position, not including internships or co-ops\n Have experience building data-dense internal tools or operator interfaces, not just marketing or consumer surfaces\n Have experience with integrity, spam, fraud, or abuse detection and mitigation\n Have experience building trust and safety detection mechanisms and intervention for AI/ML systems\n Have experience with prompt engineering, jailbreak attacks, and other adversarial inputs\n Have worked closely with operational teams to build custom internal tooling\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $405,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases,","salary_min":405000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","fine-tuning","rust","fullstack"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5369710008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T14:55:52Z","expires_at":"2026-11-13T16:54:56.598201Z","created_at":"2026-10-14T15:17:23.066476Z","updated_at":"2026-10-14T16:54:56.601343Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6146aded-8614-4fdf-a457-cef73b78ddbe"},{"id":"89d58d57-03e8-4684-83b2-3121d619b65f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, ML Serving Platform","slug":"software-engineer-ml-serving-platform-c430ad40","description":"About the Team \n DoorDash’s ML Serving Platform delivers tens of millions of predictions per second , powering search, recommendations, advertising, delivery estimates, and logistics across DoorDash, Wolt, and Deliveroo.\n Our customers are modelers and engineering teams across our internal business verticals. We build self-serve infrastructure that empowers them to bring new models into production, adopt open source software and models, and expand what they can accomplish with machine learning at scale.\n We work at the intersection of a rapidly evolving open source ecosystem and demanding production workloads. With active customer demand and growing modeling ambitions, we’re advancing the infrastructure behind today’s predictions while building the capabilities that enable the next generation of ML innovation.\n About the Role \n You’ll help build the next generation of our ML serving platform, connecting request routing and online feature retrieval with model inference on CPU and GPU infrastructure. You’ll tackle challenging infrastructure problems involving latency, reliability, resource efficiency, and scale, and make those capabilities accessible through self-serve tools and workflows.\n Working alongside experienced platform engineers, you’ll own defined projects from technical design and implementation through testing, rollout, and production support. You’ll partner directly with internal teams to understand emerging modeling requirements, remove adoption barriers, and turn advances in open source technology into measurable production improvements.\n You’re excited about this opportunity because you will… \n \n Empower modelers through self-serve infrastructure. Build tools, APIs, and workflows that help internal teams deploy, configure, validate, and operate models independently, accelerating ML adoption across business verticals.\n Bring open source innovation into production. Evaluate and integrate evolving inference frameworks and enable open source models, translating promising capabilities into reliable, efficient services at scale.\n Solve demanding inference infrastructure problems. Improve the systems that route prediction requests, retrieve online features, and execute models on a platform serving tens of millions of predictions per second.\n Help evolve our disaggregated serving architecture. Build modular components that allow routing, feature retrieval, and model execution to evolve and scale independently as workloads and modeling requirements change.\n Improve Kubernetes deployment and autoscaling. Help workloads respond to changing traffic while meeting latency and availability requirements and using CPU and GPU resources efficiently.\n Make adoption and rollout easier. Build integrations, validation, and migration tooling that help teams adopt new serving capabilities and our unified global platform with confidence.\n Own performance and reliability in production. Use benchmarking, profiling, metrics, and tracing to identify bottlenecks; participate in on-call and improve automation and runbooks to make the platform easier to operate.\n \n We’re excited about you because… \n \n You have 2+ years of software engineering experience building and maintaining production services or infrastructure.\n You have strong computer science fundamentals and proficiency in a backend or systems programming language such as Java, Kotlin, Go, C++, or Python.\n You understand distributed systems fundamentals, including concurrency, networking, timeouts, failure handling, and performance trade-offs.\n You enjoy challenging infrastructure problems and can independently turn a defined problem into a technical design, tested implementation, and safe production rollout.\n You have experience debugging production systems and using operational data to improve reliability, performance, or cost.\n You care about the engineers using your platform and collaborate effectively with customers and partners to make complex infrastructure easier to adopt.\n You hold a degree in Computer Science or a related field, or have equivalent practical experience.\n \n Nice to Have \n \n Experience with ML inference infrastructure, online feature retrieval, or other latency-sensitive distributed services.\n Experience operating containerized workloads on Kubernetes, including deployment, resource management, or autoscaling.\n Experience building self-serve developer platforms, APIs, or automation that helps other engineers move faster.\n Experience integrating open source infrastructure or models and validating their behavior under production workloads.\n Familiarity with CPU/GPU performance profiling, inference runtimes, or model deployment workflows.\n \n Applications for this position are accepted on an ongoing basis\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work lo","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["mlops","distributed-systems","fine-tuning","cloud","gpu","healthcare","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8263726","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:36Z","expires_at":"2026-11-13T17:03:28.772861Z","created_at":"2026-10-14T15:25:31.414102Z","updated_at":"2026-10-14T17:03:28.775658Z","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/89d58d57-03e8-4684-83b2-3121d619b65f"},{"id":"d6ccab06-8408-4826-b3a1-259ba586f060","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Machine Learning Platform - MDX","slug":"software-engineer-machine-learning-platform-mdx-445b9a46","description":"About the Team \n Come help us build the world's most reliable on-demand, logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.\n About the Role \n At DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search \u0026 Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale or Seattle.\n You’re excited about this opportunity because you will…\n \n Build a world-class ML platform where models are developed, trained, and deployed seamlessly\n Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases \n You will help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches\n You will work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day\n You will help design and drive directions for the centralized machine learning platform that powers all of DoorDash's business.\n Improve the reliability, scalability, and observability of our training and inference infrastructure.\n \n We’re excited about you because… \n \n B.S., M.S., or PhD. in Computer Science or equivalent\n Exceptionally strong knowledge of CS fundamental concepts and OOP languages\n 2+ years of industry experience in software engineering\n Prior experience building machine learning systems in production such as enabling data analytics at scale\n Prior experience in machine learning - you've developed and deployed your own models - even if these are simple proof of concepts\n Systems Engineering - you've built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems\n \n Nice To Haves \n \n Experience with challenges in real-time computing\n Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure\n Familiar with Pandas and Python machine learning libraries and deep learning frameworks such as PyTorch and TensorFlow\n Familiar with Spark, MLLib, Databricks,MLFlow, Apache Airflow, Dagster and similar related technologies.\n Familiar with large language models like GPT, LLAMA, BERT, or Transformer-based architectures\n Familiar with a cloud based environment such as AWS\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $130,600 — $192,000 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order fo","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["healthcare","pytorch","cloud","llm","deep-learning","distributed-systems","fine-tuning","tensorflow"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8264534","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:34Z","expires_at":"2026-11-13T17:03:28.767496Z","created_at":"2026-10-14T15:25:31.409011Z","updated_at":"2026-10-14T17:03:28.770438Z","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/d6ccab06-8408-4826-b3a1-259ba586f060"},{"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":"62316960-4081-4d09-883a-e1356f1a8463","company_id":"e455f75a-a424-4955-9844-afebe8ea6eb4","title":"Senior AI Analyst, People Digital Solutions","slug":"senior-ai-analyst-people-digital-solutions-c64475e7","description":"Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.\n The People Operations \u0026 Digital Solutions team \n The People Operations \u0026 Digital Solutions team shapes the future of employee support at Okta through innovative technology, intelligent knowledge management, and automated self-service experiences. Reporting to the Senior Director, People Operations \u0026 Digital Solutions, this team collaborates closely with People Operations, Total Rewards, Recruiting, IT, Learning, and Knowledge Management to enhance digital support across the enterprise.\n The Senior AI Analyst opportunity \n We are seeking an experienced Senior AI Analyst with a strong background in ServiceNow and Analytics to help train, evaluate, and improve AI models and AI-powered enterprise solutions. The ideal candidate will combine hands-on ServiceNow knowledge, data and analytics expertise, and strong communication skills to create high-quality training data, evaluate AI outputs, and translate business and technical requirements into effective AI training scenarios. This is a high-impact role responsible for improving how employees interact with AI while enabling support teams through intelligent knowledge management and training.\n As the subject matter expert for our AI solutions, you will analyze conversational insights, optimize AI performance, and ensure AI assistants deliver accurate, relevant, and trusted responses. You'll own the training and continuous improvement of AI-powered experiences—including an employee support chatbot, Agentic Coach, and future AI assistants—while partnering with cross-functional teams to enhance knowledge, self-service, and employee support.\n What you’ll be doing  \n \n Train, test, and evaluate AI-powered assistants, chatbots, and agentic AI solutions to ensure accurate and reliable employee experiences.\n Design and optimize prompts, conversational flows, intents, responses, and training scenarios across enterprise support platforms.\n Evaluate AI outputs for accuracy, relevance, completeness, tone, and consistency while identifying and mitigating hallucinations, response gaps, or misinformation.\n Monitor and analyze AI performance metrics such as containment, resolution, user satisfaction, engagement, and escalation rates; identify trends, emerging intents, knowledge gaps, and opportunities for automation from conversational data.\n Apply ServiceNow knowledge to translate complex business processes into intuitive conversational scenarios, training data, and evaluation criteria; collaborate with ServiceNow administrators, developers, analysts, and business stakeholders to identify automation opportunities.\n Create and maintain AI training materials, test cases, evaluation criteria, and sample conversations; partner with Knowledge Management teams to improve content quality, discoverability, and AI readiness; keep materials current as products, processes, and policies evolve.\n Support testing and rollout of new AI capabilities and enhancements; provide insights and recommendations based on AI performance, user feedback, and analytics to drive continuous improvement across the People Organization.\n Stay current with developments in generative AI, prompt engineering, conversational AI, agentic AI, and responsible AI; contribute ideas that improve employee self-service and digital support experiences.\n \n What you’ll bring to the role \n \n Experience in AI training, prompt engineering, conversational design, NLP, or AI optimization within employee or customer support environments.\n Proven experience training, evaluating, and optimizing AI models, conversational agents, or LLM-powered assistants, including agentic AI solutions.\n Strong understanding of prompt engineering, AI evaluation, model optimization, and responsible AI practices.\n Demonstrated ability to analyze conversational data and performance metrics to drive measurable improvements.\n Working knowledge of ServiceNow or a similar enterprise service-management platform.\n Strong analytical skills and experience with tools such as SQL, Excel, Power BI, Tableau, or ServiceNow Analytics.\n Experience with enterprise conversational AI platforms, knowledge management systems, or AI-enabled search technologies.\n Excellent written and verbal communication skills with the ability to translate technical and business requirements into clear, user-focused content and influence cross-functional stakeholders.\n Understanding of knowledge management, digital employee support, and omnichannel service experiences.\n \n A","salary_min":108000,"salary_max":149000,"location":"Bellevue, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","generative-ai","llm","nlp","fine-tuning"],"apply_url":"https://www.okta.com/company/careers/opportunity/8257554?gh_jid=8257554","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T13:19:08Z","expires_at":"2026-11-13T16:58:07.274356Z","created_at":"2026-10-14T15:21:47.277821Z","updated_at":"2026-10-14T16:58:07.277552Z","company_name":"Okta","company_slug":"okta","company_logo_url":"https://www.google.com/s2/favicons?domain=okta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/62316960-4081-4d09-883a-e1356f1a8463"}],"page":1,"per_page":20,"total":854,"total_is_exact":true,"total_pages":43}