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We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\n\n\nABOUT THE ROLE\n\n We're looking for a Fullstack Engineer to work on LangSmith, our commercial AI observability and evals platform product.  In this role, you'll have the opportunity to build new features and functionality for the platform while working close enterprise customers, developer end-users and internal stakeholders.\n\n\n\nWHAT YOU WILL DO\n\n - Develop and implement key product features working across our entire Go, Python, and Typescript stack\n\n - Collaborate closely with product and design teams to iterate on new features and enhance the product roadmap\n\n - Drive aggressive project timelines while ensuring high engineering standards are being met by writing clean, maintainable, and well-tested code\n\n\n\n\nHOW TO BE SUCCESSFUL IN THIS ROLE\n\n - 2+ years of experience in software engineering working on complex platform products\n\n - Fullstack engineering experience with Go or Python on the backend and React + Typescript on the frontend\n\n - Solid understanding of database systems, particularly Postgres and Redis\n\n - Experience designing and scaling APIs, preferably in high-performance, data-intensive applications\n\n - Strong ownership and a “get stuff done” mindset - comfortable shipping fast and iterating based on user feedback\n\n - Strong communicator with the ability to collaborate closely with various internal stakeholders and engage directly with our top-tier customers\n\n - Bonus: Experience with AI/LLM-powered applications, developer tools, or observability platforms\n\n\n\nCompensation\nAnnual salary range: $145,000-180,000 USD \n\nCompensation Philosophy:\n\nWe offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. \n\nWe are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.\n\n\n\n\nBENEFITS\n\nBenefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.","salary_min":145000,"salary_max":180000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["llm","agents","fullstack","evaluation"],"apply_url":"https://jobs.ashbyhq.com/langchain/ddf92275-1cc3-49c0-9f25-e8ded43b07f6/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T15:18:56.705Z","expires_at":"2026-11-13T16:55:07.883661Z","created_at":"2026-04-13T09:37:32.897623Z","updated_at":"2026-10-14T16:55:07.886796Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a3456dd3-15e8-4fdc-8daf-74e40b0d4955"},{"id":"d1f23f74-64a2-4091-ad96-0a63c47efea2","company_id":"01b03876-5b01-47ab-82b9-696293e861b9","title":"Senior AI Data \u0026 Analytics Engineer II","slug":"senior-ai-data-analytics-engineer-ii-f99113fb","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":"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":"f775cd66-297f-48c7-b9f6-9a10c329a36b","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Member of Design Staff","slug":"member-of-design-staff-41142675","description":"MEMBER OF DESIGN STAFF\n\n\n\nFirecrawl ships fast, and right now our visuals can't keep up. We're looking for a designer with real taste who builds their own AI systems to make great launch work fast. You'll work side by side with Rhys, our Creative Lead, on every launch we ship. Together you'll build Alloy, the AI workspace where our brand and creative tools live. Your work will be the first thing millions of developers see from us.\n\n\n\nSalary Range: $196,000–$220,000 USD/year\n\nEquity Range: Competitive equity. Details shared during the process.\n\nLocation: San Francisco, CA (SF HQ). In office 3+ days a week.\n\nJob Type: Full-time\n\nExperience: 5+ years\n\nWork Authorization: Must be authorized to work in the United States. We support candidates already in the US who need a transfer or existing status (H-1B transfers, F-1 OPT/STEM OPT), plus TN and E-3. We’re not able to start new H-1B sponsorship for candidates outside the US.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data. It's the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nIn September 2026, we raised a $75M Series B led by Smash Capital, and we're spending it building the largest repository of knowledge in the world. We hit 8 figures in ARR in year one and more than doubled it in year two. We have 187k+ GitHub stars, putting us in the top 40 repositories of all time, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves. No hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web. That library is called Alexandria, and it starts now.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design and animate launch videos and social assets for a constant launch cadence, so visuals are never the thing slowing a launch down.\n\n - Concept launches with Rhys from the first idea, and push back when a brief is wrong.\n\n - Build the systems that make this fast. When something takes too long by hand, you build the tool, agent workflow or reusable primitive that fixes it and add it to Alloy.\n\n - Shape our brand system in Alloy, from type and color to motion and the rules that make it feel like us.\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\nTwo things matter most.\n\n - Taste you can see in a launch video. You've made at least one launch video we'd love, with AI or without it, and you can show us exactly what you owned.\n\n - Systems you built to make it fast. You build your own tools and workflows with AI so you can make great work really quickly. You don't need an engineering background.\n\nBeyond those, we look for a few more things.\n\n - You make the call. There's no committee or approval chain. You'll work directly with Rhys and Eric, and you're expected to push back and own the outcome.\n\n - Nice to have. Range across formats like posters and illustration, and After Effects fluency.\n\nBackgrounds that often do well include a motion or brand designer at an AI or devtools startup who owns launches end to end, and a designer or creative technologist who has built their own creative tools with AI.\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Engineers who design on the side. Design is the job.\n\n - Static-only portfolios. Motion is core to the job.\n\n - Anyone who needs a long brief and a long timeline to do great work.\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings, but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense: $196,000–$220,000 USD/year, based on impact, not tenure\n\n - Own a piece: Gain competitive equity in what you're helping build\n\n - Generous PTO: 15 days mandatory, anything after 24 days, just ask (holidays excluded). Take the time you need to recharge\n\n - Parental leave: 12 weeks fully paid, for all parents\n\n - Wellness stipend: $100 USD/month for the gym, therapy, massages, or whatever keeps you human\n\n - Learning \u0026 Development: Expense up to $1,000 USD/year toward anything that helps you grow professionally\n\n - Team offsites: A change of scenery, minus the trust falls\n\n - Sabbatical: 3 paid months off after 4 years, do something fun and new\n\n\n\n\nAVAILABLE TO US-BASED FULL-TIME EMPLOYEES\n\n - Full coverage, no red tape: Medical, dental, and vision (100% for employees, 50% for partner and kids). No weird loopholes, just care that works\n\n - Life \u0026 Disability insurance:","salary_min":196000,"salary_max":220000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["payments","agents","data-pipeline","llm"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/36fa90fa-69c0-4591-ad98-435738f36925/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T03:25:22.913Z","expires_at":"2026-11-13T17:00:14.091632Z","created_at":"2026-10-14T15:23:48.967274Z","updated_at":"2026-10-14T17:00:14.094755Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f775cd66-297f-48c7-b9f6-9a10c329a36b"},{"id":"2b1514f0-20bf-4830-9fdc-23e5c0f8bbed","company_id":"a25140f5-772d-4c47-947d-7c655ed80737","title":"Senior Software Engineer - Normalization Platform","slug":"senior-software-engineer-normalization-platform-451aa2a7","description":"Senior Software Engineer - Normalization Platform \n Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care. Achieving Truveta’ s ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values . \n This position is based out of our headquarters in the Greater Seattle area.  #LI-hybrid \n Who We Need   \n Truveta is rapidly building a talented and diverse team to tackle complex health and technical challenges. We are seeking candidates inspired by the opportunity to apply data in the development of real-world health solutions. Beyond core capabilities, we are seeking problem solvers, passionate and collaborative teammates, and those willing to roll up their sleeves while making a difference.     \n If you are interested in the opportunity to pursue purposeful work, join a mission-driven team, and build a rewarding career while having fun, Truveta may be the perfect fit for you.  \n This Opportunity   \n Truveta's mission is Saving Lives with Data: helping researchers find cures faster, empowering every clinician to be an expert, and helping families make the most informed decisions about their care. Semantic normalization is where that data becomes usable. Health systems describe the same diagnosis, lab result, or medication in countless ways, and our platform maps each one to a standard medical concept, so researchers can study data from many health systems as one. Every dataset Truveta delivers runs through it.   \n As a Senior Software Engineer, you will own major pieces of the platform's next-generation architecture. You will scale the service for both the volume and the variety of its workloads, building a flexible system that keeps pace with Truveta's growing data volume and expanding range of data sources.   \n We are seeking backend engineers who: \n \n \n Have proven experience building reliable, secure, and scalable software solutions using modern cloud-based paradigms   \n Are quick learners who can iterate on and evolve distributed software solutions while carefully balancing privacy, security, and reliability requirements with a changing product landscape   \n Have knowledge of and experience designing and working with distributed systems, data pipelines, and APIs   \n Can guide workstreams, mentor and inspire junior developers, and cultivate a positive work environment   \n Adhere to Truveta principles in support of ethical innovation   \n Are committed to ongoing training on key topics such as privacy, ethics, and security   \n \n Key Qualifications:   \n \n 5+ years of experience building production-quality software in a team setting   \n Experience building cloud services or data pipelines in Java, Python, or C#   \n Experience building and supporting cross-platform, multi-tenant services   \n 2+ years of experience on cloud-native platforms (AWS, GCP, or Azure)   \n B.S. or M.S. in Computer Science or a related field   \n InfraOps or DevOps experience is a plus   \n Kubernetes experience is a plus   \n Healthcare domain experience is a plus   \n Experience with AI agents is a plus   \n \n Why Truveta? \n Be a part of building something special. Now is the perfect time to join Truveta. We have strong, established leadership with decades of success. We are well-funded. We are building a culture that prioritizes people and their passions across personal, professional, and everything in between. Join us as we build an amazing company together. \n We Offer: \n \n Interesting and meaningful work for every career stage \n Great benefits package \n Comprehensive benefits with strong medical, dental and vision insurance plans \n 401K plan \n Professional development \u0026 training opportunities for continuous learning \n Work/life autonomy via flexible work hours and flexible paid time off \n Generous parental leave \n Regular team activities (virtual and in-person) \n The base pay for this position is $155,000 to $190,000. The pay range reflects the minimum and maximum target. Pay is based on several factors including location and may vary depending on job-related knowledge, skills, and experience. Certain roles are eligible for additional compensation such as incentive pay and stock options. \n \n If you are based in California, we encourage you to read this important information for California residents linked here. \n Truveta is committed to creating a diverse, inclusive, and empowering workplace. We believe that having employees, interns, and contractors with diverse backgrounds enables Truveta to better meet our mission and serve patients and health communities around the world. We recognize that opportunities in technology historically excluded and continue to disprop","salary_min":155000,"salary_max":190000,"location":"Seattle, WA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","healthcare","distributed-systems","data-pipeline","platform"],"apply_url":"https://job-boards.greenhouse.io/truveta/jobs/6223732004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T23:29:02Z","expires_at":"2026-11-13T17:00:07.879355Z","created_at":"2026-10-14T15:23:39.300558Z","updated_at":"2026-10-14T17:00:07.882558Z","company_name":"Truveta","company_slug":"truveta","company_logo_url":"https://www.google.com/s2/favicons?domain=truveta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2b1514f0-20bf-4830-9fdc-23e5c0f8bbed"},{"id":"ebabc8b3-5891-442b-a4d0-7a3ac1388c2d","company_id":"aa372131-86ce-432a-af45-e2b42a79ba29","title":"Infrastructure Engineer, Agentic DevSecOps","slug":"infrastructure-engineer-agentic-devsecops-35467747","description":"ABOUT DISTYL AI \n\nDistyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.\n\nWe research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.\n\nDistyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. \n\n\n\n\nWHAT WE ARE LOOKING FOR\n\nWe're hiring a Infrastructure Engineer to own how Distyl engineers build, test, secure, and release software. Our independently versioned services span multiple repositories and deploy into customer environments across AWS, Azure, and GCP, with different security, connectivity, and upgrade constraints.\n\n\n\nThis is a hands-on role with scope matched to your experience, from owning complex delivery systems to setting technical direction across teams. You'll build shared delivery systems and coding-agent workflows that make releases reliable, failures actionable, and engineers more productive. Your systems will shape how every Distyl engineer ships.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Technical ownership. Set direction and priorities for build, test, release, and coding-agent infrastructure. Choose tools and standards with engineering partners, and own implementation, adoption, and operation across teams\n\n - Build systems and repository standards. Standardize repository conventions and reusable build/test workflows. Make builds reproducible and efficient through explicit dependencies, consistent tooling, and effective caching\n\n - Release engineering. Build once and promote versioned bundles of immutable artifacts through release stages, with source traceability and test and security gates. Partner with Platform, Runtime, and Customer Delivery on integration and compatibility testing, configuration and schema changes, and recovery. Support customer-registry distribution and offline packaging where required\n\n - Agent-enabled engineering. Equip engineers with reusable coding-agent tools to diagnose failures, propose fixes, and handle routine work. Include tests, scoped permissions, failure handling, and independent output validation; measure quality and time saved\n\n - Software supply-chain security. Implement artifact signing and provenance, software bills of materials (SBOMs), isolated build runners, scoped CI credentials, and vulnerability gates. Integrate dependency and container remediation into engineering workflows\n\n - Developer experience. Understand engineers' workflows and remove friction with self-service tools, documentation, and diagnostics that distinguish code failures from tooling issues. Use developer feedback to guide incremental migrations, earn adoption, and reduce manual work\n\n - Engineering productivity and alerting. Track build/test latency, pipeline reliability, flaky-test rates, release lead time, and tooling adoption. Use these signals and developer feedback to reduce flaky tests and bottlenecks. Set service-level objectives and route actionable alerts to owners\n   \n   \n\n\nWHO YOU ARE\n\n - 10+ years of experience in software engineering, developer productivity, SRE, or related infrastructure engineering roles\n\n - Experience enabling engineers to use coding agents through reusable tools, workflows, and guidance, with independent validation and scoped access\n\n - Experience owning production build, test, and release infrastructure across services and teams, including versioned release bundles and compatibility testing\n\n - Hands-on experience with build systems, dependency management, reproducibility, performance diagnosis, and caching\n\n - Strong Python skills for building and debugging backend services and maintainable automation. Comfortable navigating unfamiliar code, including JavaScript frontends\n\n - Strong Linux fundamentals and experience building and maintaining secure container images. Working knowledge of Kubernetes, GitOps, infrastructure as code, and cloud deployment\n\n - Hands-on experience implementing software supply-chain controls. Demonstrated security judgment across permissions, credentials, dependencies, artifacts, and remediation\n\n - Experience building developer platforms, automated remediation, or reusable agent tooling adopted by other engineers\n\n - A record of improving build/test performance, release reliability, and tooling adoption using metrics and developer feedback\n\n - Experience","salary_min":200000,"salary_max":250000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["microservices","agents","healthcare","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/distyl/6b13df85-e705-4a69-9340-0e83c54e78c5/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T23:19:53.802Z","expires_at":"2026-11-13T17:03:04.859555Z","created_at":"2026-10-14T15:25:08.324287Z","updated_at":"2026-10-14T17:03:04.863136Z","company_name":"Distyl AI","company_slug":"distyl-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=distyl.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ebabc8b3-5891-442b-a4d0-7a3ac1388c2d"},{"id":"972adbf0-5b20-4db8-9e6c-f42932f2c665","company_id":"76c63eb7-c307-4322-8c2b-c20216feec49","title":"Principal Software Engineer, Agentic Data Platform","slug":"principal-software-engineer-agentic-data-platform-70ab45e1","description":"Get to Know Us\n\nHorizon3.ai http://Horizon3.ai is a fast-growing, remote cybersecurity company dedicated to the mission of enabling organizations to proactively find, fix and verify exploitable attack vectors before criminals exploit them. Our flagship product, the NodeZero™ platform, delivers production-safe autonomous pentests and other key assessment operations that scale across the largest internal, external, cloud, and hybrid cloud environments. NodeZero has been adopted by organizations of all sizes, from small educational institutions to government agencies and Global 100 enterprises. It is used by IT Ops/SecOps teams, consulting pentesters, and MSSPs and MSPs.\n\nWe are a fusion of former U.S. Special Operations cyber operators, startup engineers \u0026 operators, and formerly frustrated cybersecurity practitioners. We're committed to helping solve our common security problems: ineffective security tools and false positives, resulting in alert fatigue, blind spots, \"checkbox\" security culture, cybersecurity skills shortage, and the long lead time and expense of hiring outside consultants. Collectively, we are a team of learn-it-alls, committed to a culture of respect, collaboration, ownership, and results.\n\nAs a remote first company, we require minimum 25Mbps consumer grade broadband connection.\n\n \n\nWhat You'll Do\n\nThe Principal Engineer for Database Platform is the technical anchor of NodeZero's data infrastructure. This is an individual contributor role with no direct reports — your influence is through technical leadership, architecture ownership, and raising the engineering bar across the team. You own the most consequential database architecture decisions, define the standards that govern how NodeZero stores, queries, and migrates data at scale, and ensure the data layer can support NodeZero's growth — more customers, larger environments, more pentest data — without requiring proportional operational effort. This is a deeply hands-on role: you will write code, author design documents, and set the technical direction that every engineer on the Database Platform team builds toward.\n\nNodeZero's data layer is not simple. We store millions of discovered assets, graph-structured attack paths, time-series vulnerability data, and audit-critical pentest results — across a multi-tenant architecture where correctness and data isolation are non-negotiable. You will be the person who makes the hardest data architecture calls and ensures the team has the technical foundations to build reliably on.\n\n - Technical Strategy: Define and drive the technical roadmap for the Database Platform in partnership with the Senior Engineering Manager and the broader Pentest Platform organization.\n\n - Architecture Leadership: Own the most complex database architecture decisions — schema design, partitioning strategies, migration patterns, and scalability approaches. Produce design documents that become the shared technical model for the team.\n\n - PostgreSQL Excellence: Be NodeZero's definitive expert on PostgreSQL at scale — query optimization, indexing, partitioning, replication, and connection pooling. Set the standards the team follows and catch the problems others miss.\n\n - Graph \u0026 Multi-model Data: Architect and guide the evolution of NodeZero's graph-based asset model — the data structure that represents how discovered hosts, services, credentials, and vulnerabilities relate to each other across pentests.\n\n - Schema Governance: Own schema design review processes. Ensure every migration is safe, reversible, and zero-downtime. Build the tooling and culture that treats schema changes with the same rigor as production code changes.\n\n - Mentorship \u0026 Bar Raising: Mentor P5 and P4 engineers through code review, design review, and pairing. Be the person on the team that raises everyone else's ceiling.\n\n - Cross-functional Influence: Partner with Data Ingestion, Asset Inventory, API Platform, and Security Research to define data contracts, query performance standards, and architectural guardrails that span the full data pipeline.\n\n \n\nWhat You'll Bring\n\nTechnical Depth\n\n - Deep, production-proven PostgreSQL expertise: query planner internals, advanced indexing (partial, expression, GiST/GIN), partitioning strategies, logical replication, vacuuming at scale, and connection pool tuning.\n\n - Experience architecting multi-tenant databases at scale: row-level security, tenant isolation, schema-per-tenant vs. shared-schema tradeoffs.\n\n - Experience with schema migration at scale in production systems: zero-downtime migrations, backwards-compatible schema evolution, and migration tooling (Flyway, golang-migrate, or equivalent).\n\n - Familiarity with graph data models and graph databases (Neo4j, AWS Neptune) or graph-like query patterns in relational systems (recursive CTEs, ltree, adjacency list).\n\n - Proficient in Go or Python — able to write production application code, not just SQL.\n\nArchitecture \u0026 Design\n\n - Demonstrate","salary_min":255000,"salary_max":290000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["security","agents","cloud","data-pipeline","platform"],"apply_url":"https://jobs.ashbyhq.com/horizon3ai/61bab297-9f6c-40d8-a7af-99ff61066943/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T20:23:08.726Z","expires_at":"2026-11-13T16:56:49.961684Z","created_at":"2026-10-14T15:20:34.974801Z","updated_at":"2026-10-14T16:56:49.964715Z","company_name":"Horizon3 AI","company_slug":"horizon3-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=horizon3.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/972adbf0-5b20-4db8-9e6c-f42932f2c665"},{"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":"1d5ba28a-37f9-4138-9c77-647324c5d234","company_id":"b6e5a3d1-9bde-4a82-8d78-9f38ed99ee81","title":"Research Manager - Post-Training for Agents","slug":"research-manager-post-training-for-agents-5de82862","description":"As a Research Manager within Datadog AI Research (DAIR), you will lead a team working on fundamental research in post-training and autonomous agents while remaining deeply engaged in the technical direction of the work. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will provide technical leadership to a team of researchers and research engineers, mentor and develop highly technical talent, shape research direction, and collaborate with Datadog’s product and engineering teams to translate research advances into products. This role is designed for an exceptional researcher or technical leader who wants to maintain a strong hands-on research identity while developing and leading a high-performing team.  \n At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.  \n What You'll Do:  \n \n Lead research in agent post-training and reinforcement learning, setting the technical direction for ambitious research programs grounded in observability and security\n Guide and mentor researchers and research engineers while remaining closely involved in research strategy, experimentation, model development, and technical problem-solving \n Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments \n Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement\n Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities\n Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks \n \n Who You Are:  \n \n You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling \n You have demonstrated the ability to lead technically ambitious research at meaningful scale, whether in an industry research lab, startup, academic environment, or another research setting \n You have extensive hands-on experience designing or implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks\n You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements \n You have operated at a senior technical level and have experience leading, mentoring, and developing teams of researchers or engineers, while remaining deeply hands-on in technical direction and execution \n You want to develop and support a research team while maintaining a strong technical identity, and you can communicate complex research findings effectively across technical and non-technical audiences \n \n Datadog values people from all walks of life. We know not everyone will meet all the above qualifications on day one. That’s okay. If you’re passionate about technology and want to grow your experience, we encourage you to apply.  \n Benefits and Growth:  \n \n Competitive global benefits \n New hire stock equity (RSUs) and employee stock purchase plan (ESPP)\n Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris \n Opportunity to attend and present at conferences and meetups \n Intra-departmental mentor and buddy program for in-house networking\n An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)  \n \n Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. \n #LI-Hybrid\n Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.\n The reasonably estimated yearly salary for this role at Datadog is:\n $244,000 — $305,000 USD \n \n About Datadog:  \n Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applic","salary_min":244000,"salary_max":305000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","search","agents","healthcare","data-pipeline","research"],"apply_url":"https://careers.datadoghq.com/detail/8279173/?gh_jid=8279173","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:57:23Z","expires_at":"2026-11-13T16:55:38.246957Z","created_at":"2026-10-14T15:19:28.467785Z","updated_at":"2026-10-14T16:55:38.249824Z","company_name":"Datadog","company_slug":"datadog","company_logo_url":"https://www.google.com/s2/favicons?domain=datadoghq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1d5ba28a-37f9-4138-9c77-647324c5d234"},{"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":"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":"08abe8d6-65e3-466e-8f29-1901fbeb8e57","company_id":"66e863fb-9aaf-40df-996c-eb439e6f857e","title":"Software Engineer, Agentic Runtime ","slug":"software-engineer-agentic-runtime-2580af99","description":"About Glean: \n  \n Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. \n  \n At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. \n  \n Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. \n  \n If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more. You'll be deeply embedded where people get things done. You'll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. \n  \n About the Role: \n  \n The Agentic Runtime team builds the low‑latency, reliable, and secure foundation that powers Glean’s AI agents and assistant experiences at scale. You’ll design and operate core runtime services for multi‑turn orchestration, tool calling, model routing, memory, streaming, and safety. You’ll work across distributed systems, production observability, and ML infra integrations to deliver an experience that feels instant, accurate, and trustworthy — while optimizing cost and reliability. \n  \n You will: \n \n \n Own impactful runtime problems end‑to‑end — from architecture and design to production launch and ongoing reliability. \n Build and evolve core services for session lifecycle, streaming responses (e.g., gRPC/WebSockets), structured tool execution, memory/state, and policy/guardrails. \n Design for performance, correctness, and cost: reduce p50/p95 latency, improve tail behavior, and optimize token/tool budgets. \n Integrate with leading LLM providers (e.g., OpenAI, Anthropic, Google Gemini) and internal evaluation frameworks to improve quality and predictability. \n Harden the platform with fault isolation, retries, timeouts, circuit‑breaking, backpressure, and graceful degradation. \n Instrument deep observability (tracing, metrics, logs) and create playbooks/SLOs for high availability and on‑call excellence. \n Collaborate closely with product, quality, and application teams to prioritize the most impactful roadmap investments. \n \n You are: \n \n \n 3+ years of software engineering experience building production distributed systems or cloud‑native applications. \n BS/BA in Computer Science or related field, or equivalent practical experience. \n Strong coding skills in at least one of: Python, Go, Java, Javascript, Typescript, C++, with a focus on reliability, performance, and tests. \n Product‑minded: you prioritize customer impact, clear SLAs/SLOs, and pragmatic iteration. \n Ownership‑driven with a positive, proactive attitude; comfortable leading projects or learning from battle‑tested engineers. \n \n \n Experience operating services on Kubernetes and at least one major cloud (e.g., GCP, AWS, or Azure). \n Familiarity with event/streaming systems (e.g., Pub/Sub, Kafka), caching (e.g., Redis), and data stores for low‑latency paths. \n Practical understanding of LLM/agents building blocks: tool/function calling, structured outputs, streaming, and model selection/routing. \n Strong observability and debugging skills: tracing (e.g., OpenTelemetry), metrics, dashboards, and production forensics. \n Background in one or more areas is a plus: policy/guardrails, multi‑tenant isolation, rate‑limiting, concurrency control, cost optimization. \n \n Location:   \n \n \n This role is hybrid (4 days a week in our Mountain View office) \n \n Compensation \u0026 Benefits: \n  \n The standard base sa","salary_min":180000,"salary_max":210000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["api-design","agents","llm","distributed-systems","cloud"],"apply_url":"https://job-boards.greenhouse.io/gleanwork/jobs/4742953005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T23:36:06Z","expires_at":"2026-11-13T16:55:35.324585Z","created_at":"2026-10-14T15:19:25.450729Z","updated_at":"2026-10-14T16:55:35.327737Z","company_name":"Glean","company_slug":"glean","company_logo_url":"https://www.google.com/s2/favicons?domain=glean.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/08abe8d6-65e3-466e-8f29-1901fbeb8e57"},{"id":"9cb8cda9-490d-4d6a-9dde-db4aec8490dd","company_id":"66e863fb-9aaf-40df-996c-eb439e6f857e","title":"Tech Lead Manager, AI Evaluations","slug":"tech-lead-manager-ai-evaluations-613503c9","description":"About Glean: \n  \n Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. \n  \n At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. \n  \n Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. \n  \n If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more. You'll be deeply embedded where people get things done. You'll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. \n About the Role: \n The bottleneck in building reliable and useful AI systems isn’t writing code anymore. It’s knowing what good looks like, applying that knowledge and experience from production usage into building rigorous evals, and using those evals to guide continuous product self-improvement loops. \n As the Tech Lead Manager of AI Evaluations, you will lead a team of applied AI scientists and engineers and build the evaluation strategy and platform that helps Glean ship delightful and reliable AI experiences. You will make evaluation a continuous part of how we develop and operate models, harnesses, assistants, agents, and other AI products at Glean. \n You’ll guide the team in translating emerging AI/ML research into reliable evaluation systems, build scalable, efficient and reusable eval infrastructure and trusted quality signals, and work across R\u0026D, product, and marketing teams to turn evaluation results into better products and customer outcomes. \n You will: \n \n Own the AI evaluation eng roadmap and technical direction end to end: from defining what success means and building the evaluation platform to integrating results into release and product decisions. \n Build scalable, reusable evaluation infrastructure for datasets, experiments, graders, trace capture and replay, result analysis, and dashboards. Make evaluations reproducible, versioned, cost-aware, and easy for product and engineering teams to use. \n Create and maintain representative evaluation sets from expert-curated examples, appropriately governed production signals, synthetic cases, and historical failures. Cover common workflows, edge cases, long-tail requests, adversarial inputs, and important user and domain slices; protect held-out acceptance sets from tuning leakage. \n Define a balanced quality measurement system for AI products, including correctness, groundedness, retrieval and context relevance, completeness, instruction following, citation quality, user preference, and task success. For agents, evaluate tool selection and arguments, multi-step trajectories, policy compliance, recovery, and final task state, not just the final response. \n Develop a fit-for-purpose mix of deterministic checks, model-based graders (including LLM-based autograders), and human evaluation. Establish clear rubrics; validate and calibrate automated judges against human judgments; and measure evaluator reliability and limitations so teams can trust and interpret scores. \n Integrate evaluation into development and operations: fast CI regression checks, broader pre-release suites, model and prompt comparisons, and sampled production evaluation. Build feedback loops from user feedback and production traces into new test cases, and use monitoring, canaries, and experiments to detect regressions and drift. \n Make quality tradeoffs measurable by connecting evaluation","salary_min":250000,"salary_max":350000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["cloud","llm","distributed-systems","alignment","agents","generative-ai","evaluation"],"apply_url":"https://job-boards.greenhouse.io/gleanwork/jobs/4742957005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:27:22Z","expires_at":"2026-11-13T16:55:35.432797Z","created_at":"2026-10-14T15:19:25.54086Z","updated_at":"2026-10-14T16:55:35.435547Z","company_name":"Glean","company_slug":"glean","company_logo_url":"https://www.google.com/s2/favicons?domain=glean.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9cb8cda9-490d-4d6a-9dde-db4aec8490dd"},{"id":"6aceb6c9-aa17-4cd3-81ca-d26284538246","company_id":"5b08d89d-9846-4ef7-8b53-d07ef3ee6c19","title":"Software Engineer, ML Infrastructure","slug":"software-engineer-ml-infrastructure-f2fd5868","description":"About Arc Institute \n Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.\n Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.\n Our two Institute Initiatives reflect this model in action:\n \n Virtual Cell Initiative : Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.\n Alzheimer's Disease Initiative : Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.\n \n More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.\n About the position \n You'll join Arc's Infrastructure Engineering team and own the data and evaluation of ML infrastructure for Arc's virtual cell models. You'll start with two systems: our Model Performance Dashboard, which benchmarks those models, and the pipeline that turns single-cell data into their training sets. You'll set the technical direction for both and connect them, so every new dataset version is judged by what it does to the models. Later in your first year, the role widens into the engineering that makes the ML team's training and evaluation run fast and at scale, lab-in-the-loop infrastructure, validation frameworks for agent-written software, and Arc's rules for what research data we bring in, keep and share. The role consolidates ownership that is split across several teams today. \n Why this position could be the best job you’ve ever had \n \n You will contribute to building a virtual cell. Your evals show which data makes Arc's models better, and the answer shapes which experiments Arc's labs run next.\n You will own the platform end to end, and you work directly with the ML researchers and scientists who rely on it.\n \n About you \n \n You finish what you own, and you fix what's broken without being asked.\n You're curious about the biology, enough to learn single-cell perturbation data and argue about what an eval should measure. \n You're steady when priorities shift. Research is high-risk, so you test ideas early and change course when the evidence says to.\n You don't wait on scarce expert time. If proving a dataset's value means running the model evals yourself, you run them.\n \n In this position you will \n \n Own how Arc measures the performance of its virtual cell models, starting with the backend of our Model Performance Dashboard, which you'll take over within your first two months. \n Decide with the ML team which evaluation tools they need, then design and build them so researchers can evaluate and compare models themselves, and measure what each dataset version does to model performance.\n Redesign the pipeline that turns single-cell data into training sets for our virtual cell models, then run it with AI agents: agents find, process and curate datasets, and automated quality checks, not manual review, decide whether a dataset version is ready for model training.\n Own the lab-in-the-loop infrastructure that turns model performance results into the case for what Arc's labs should test next, working directly with the science leads who design those experiments . \n Build or buy validation frameworks (tests, evals, QC gates, policies) that let agent-written code and apps ship without line-by-line human review. Start with our team, where you'll also own how we work with coding agents, then extend them to apps that scientists across Arc build themselves.\n Own the engineering that makes the ML team's training and evaluation run fast, reliably and at scale. The ML team owns the research and the models. Once a model is ready to push, it enters the train-and-eval CI system you own, which feeds our Model Performance Dashboard. You'll also make multi-node training a supported path, track down distributed-training failures, and measure and raise throughput and GPU utilization (MFU) on clusters that Arc IT operates.\n \n Requirements \n \n Bachelor’s degree with 8 years of relevant experience, or Master’s degree with 6 years of relevant experience.\n You've owned a production data or ML platform that researchers depend on, end to end (pipelines, storage, versioning), and set its technical direction.\n You design and build systems with AI tools and agents, and you make their output verifiable with evals, QC gates or tests before anyone relies on it.\n ","salary_min":191000,"salary_max":237000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","healthcare","pytorch","distributed-systems","agents","machine-learning","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/arcinstitute/jobs/6222681004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:16:26Z","expires_at":"2026-11-13T17:03:53.841883Z","created_at":"2026-10-14T15:25:57.647484Z","updated_at":"2026-10-14T17:03:53.845803Z","company_name":"Arc Institute","company_slug":"arc-institute","company_logo_url":"https://www.google.com/s2/favicons?domain=arcinstitute.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6aceb6c9-aa17-4cd3-81ca-d26284538246"},{"id":"9f3b22a7-0295-41b4-9648-e40148bbd237","company_id":"5b08d89d-9846-4ef7-8b53-d07ef3ee6c19","title":"Software Engineer, Lab Platform","slug":"software-engineer-lab-platform-eec06b9e","description":"About Arc Institute \n Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.\n Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.\n Our two Institute Initiatives reflect this model in action:\n \n Virtual Cell Initiative : Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.\n Alzheimer's Disease Initiative : Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.\n \n More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.\n About the position \n You'll join Arc's Infrastructure Engineering team and own support and operations for our laboratory information management system (LIMS). Arc's scientists record their samples and experiments there, including the variables they change from one experiment to the next. That record is business intelligence for the lab: it's how scientists learn which conditions improve an assay, and it also feeds the pipelines that process lab data automatically. You'll run the request queue, make schema changes, train users and maintain the checks on data as it's entered, and you'll turn the requests that keep coming back into automation. You don't need LIMS experience: strong Python and SQL and an interest in lab data matter more, and we'll teach you the platform. You'll start under our lab platform Tech Lead, who will review your work and mentor you.\n Why this position could be the best job you’ve ever had \n \n Your work reaches the lab within a week, and it shows up in results: the experiment records you keep accurate are how labs find what makes their assays perform better.\n You'll learn how a research institute turns lab work into data, with a Tech Lead whose goal is to hand you more of the system as you grow.\n \n About you \n \n You finish what you own, and you fix what's broken without being asked.\n You're curious about the biology and the mission. You want to know what a guide or a 10x Flex run is.\n You go to the scientists. You sit down with them, learn how they run and record their experiments, and understand what they need before you propose a fix.\n You're careful and thoughtful. You want more scope and you'll take it on before you have every skill it needs, and you double-check changes other teams depend on.\n You see which requests need a general solution, and you balance that work with the rest of the queue.\n \n In this position you will \n \n Run the request queue for Arc's lab data systems, from questions and fixes to schema changes, which you'll ship through our change workflow so other teams' pipelines don't break.\n Support the templates scientists record experiments with, and the command-line tools that pull their data for analysis.\n Run training sessions for scientists, and write the docs they come back to.\n Build and maintain the checks that catch errors in data as scientists enter it, so bad records don't reach analysis or pipelines.\n Help carry out the refactor of the team and permission structure in our LIMS, which decides who can see and edit what.\n Turn the requests that keep coming back into automation, and choose the approach that fits the problem: a script, a scheduled job, a template change or an AI agent. Write the runbook, add the checks and retire the manual step; changes go through the team's code review.\n By the end of your first year you will\n Build tools that make screen and experiment metadata queryable across systems for computational biologists and lab teams.\n Run the request queue and ship schema changes on your own, with the Tech Lead reviewing designs instead of each change.\n Own a part of the lab platform from design through support.\n \n Requirements \n \n Bachelor’s degree with 0-2 years of relevant experience, or an advanced degree.\n You write solid Python and SQL, and you're comfortable working with REST APIs.\n You debug problems in systems you didn't build, working from the code, logs and data.\n You use AI coding tools day to day or are ready to learn, you check what they produce, and you're careful with sensitive research data.\n You explain technical things clearly to scientists, and you write docs and training that people use.\n You ask for help early and take feedback well.\n You've owned a piece of work from start ","salary_min":118000,"salary_max":146000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","healthcare","agents","api-design"],"apply_url":"https://job-boards.greenhouse.io/arcinstitute/jobs/6222737004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:16:12Z","expires_at":"2026-11-13T17:03:53.836624Z","created_at":"2026-10-14T15:25:57.643168Z","updated_at":"2026-10-14T17:03:53.839898Z","company_name":"Arc Institute","company_slug":"arc-institute","company_logo_url":"https://www.google.com/s2/favicons?domain=arcinstitute.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9f3b22a7-0295-41b4-9648-e40148bbd237"},{"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":"32e747ed-8b79-450b-9035-c6baf08039fa","company_id":"82d2abc2-444c-4d89-9646-4739e72d700d","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-2cccd4cc","description":"About Checkr Checkr is building the data platform to power safe and fair decisions. Over 140,000 companies and millions of people rely on Checkr for AI verification in the moments that matter most: getting a new job, a new place to live, a car ride, childcare, even a date. Customers include Uber, Pennymac, Airbnb, Doordash, and Anthropic. We’re a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on Forbes Cloud 100 2025 List and is a Y Combinator 2024 Breakthrough Company .\n About the team/role \n We’re hiring an ML Engineer (P3) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data \u0026 ML organization within Engineering.\n Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.\n This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.\n This role sits in the central Data \u0026 ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.\n We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.\n What you’ll do \n \n Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.\n Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.\n Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.\n Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.\n Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.\n Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.\n \n  \n What you bring \n \n A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience\n 6+ years building software professionally, with at least 2 of those building ML systems that run in production\n Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation\n You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on\n Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal\n You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it\n An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes. \n \n Nice to have \n \n Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar)\n Background in document processing, OCR, or information extraction\n Experience with PySpark or large-scale data processing\n Ruby experience (Checkr’s platform runs on Rails)\n Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)\n Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools\n Pay Transparen","salary_min":176000,"salary_max":207000,"location":"Denver, CO","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","code-generation","payments","legal","llm","mlops","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/checkr/jobs/8274246","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T16:04:33Z","expires_at":"2026-11-13T16:58:42.655818Z","created_at":"2026-10-14T15:22:20.04347Z","updated_at":"2026-10-14T16:58:42.658798Z","company_name":"Checkr","company_slug":"checkr","company_logo_url":"https://www.google.com/s2/favicons?domain=checkr.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/32e747ed-8b79-450b-9035-c6baf08039fa"},{"id":"e134d960-5a8f-48f6-a055-10527e302063","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (APAC)","slug":"solution-architect-apac-7456acbf","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE:\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\n\n\nKEY RESPONSIBILITIES:\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect, Deployed Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communica","salary_min":185000,"salary_max":260000,"location":"Singapore","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["llm","rag","api-design","agents"],"apply_url":"https://jobs.ashbyhq.com/langchain/c882af7e-59aa-4017-b791-9a8094c0ef6f/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-10T16:57:17.138Z","expires_at":"2026-11-13T16:55:08.058735Z","created_at":"2026-08-25T18:26:42.877158Z","updated_at":"2026-10-14T16:55:08.06149Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e134d960-5a8f-48f6-a055-10527e302063"},{"id":"a6cb1a62-a2ae-4b86-bb0a-c7210c241a8f","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (NYC)","slug":"solution-architect-nyc-299c723c","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\nKEY RESPONSIBILITIES\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communication skills with abilit","salary_min":170000,"salary_max":300000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","agents","api-design","rag"],"apply_url":"https://jobs.ashbyhq.com/langchain/f71210f9-12e1-4726-88b9-ebafa194d5b2/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:09:49.395Z","expires_at":"2026-11-13T16:55:07.987572Z","created_at":"2026-08-25T18:26:42.786487Z","updated_at":"2026-10-14T16:55:07.990281Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a6cb1a62-a2ae-4b86-bb0a-c7210c241a8f"},{"id":"33db21e5-9d88-4de6-b2d0-3c134c649a65","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Deployed Architect, Professional Services (Austin)","slug":"solution-architect-austin-318d756c","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Deployed Architect to join our Professional Services team. You'll work directly with enterprise customers to design, deploy, and optimize production-grade AI infrastructure and agent systems. You'll be responsible for architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that solve real business problems.\n\nThis role combines software development, infrastructure/platform engineering, and customer-facing skills. You'll work on everything from Kubernetes cluster design to multi-agent system architecture, requiring deep technical expertise across both infrastructure and agent engineering domains.\n\nThis role offers direct impact on customer success, the opportunity to shape best practices, and work with cutting-edge AI technology. You'll join a collaborative team environment with a strong engineering culture.\n\n\n\nKEY RESPONSIBILITIES\n\n - Infrastructure \u0026 Platform Engineering: Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines\n\n - Agent Engineering \u0026 Development: Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations\n\n - Customer Engagement \u0026 Assessment: Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams\n\n\nWHAT WE'RE LOOKING FOR\n\n\n\nREQUIRED EXPERIENCE\n\n7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect or Forward Deployed Engineer. We also like former founders, so if you have an unusual background, but all the right skillsets, you are welcome to apply\n\nInfrastructure \u0026 Platform:\n\n - 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)\n\n - Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments\n\n - Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices\n\n - Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing\n\n - Experience designing high-availability and disaster recovery solutions\n\n - Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)\n\n - Experience with CI/CD pipelines for infrastructure and applications\n\nAgent Engineering \u0026 Development:\n\n - 1+ years of experience building production AI/ML applications or agents\n\n - Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications\n\n - Experience with state management patterns (short-term and long-term memory)\n\n - Experience designing and implementing evaluation frameworks for AI applications\n\n - Strong prompt engineering skills with experience in optimization and A/B testing\n\n - Experience with vector stores, RAG patterns, and knowledge organization\n\n - Experience with tool integration, API design, and error handling patterns\n\n - Strong Python and/or TypeScript development skills\n\nCustomer-Facing:\n\n - Customer-facing experience with enterprise customers\n\n - Experience conducting technical assessments or infrastructure audits\n\n - Strong communication skills with abilit","salary_min":170000,"salary_max":270000,"location":"Austin, TX","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["llm","api-design","agents","rag"],"apply_url":"https://jobs.ashbyhq.com/langchain/da7cbabd-ceed-4d7d-ae25-f226cac0c1c1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:09:36.459Z","expires_at":"2026-11-13T16:55:07.99297Z","created_at":"2026-08-25T18:26:42.792069Z","updated_at":"2026-10-14T16:55:07.996563Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/33db21e5-9d88-4de6-b2d0-3c134c649a65"}],"page":1,"per_page":20,"total":3001,"total_is_exact":true,"total_pages":151}