{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"a3456dd3-15e8-4fdc-8daf-74e40b0d4955","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"FullStack Engineer,  AI Observability \u0026 Evals Platform (LangSmith)","slug":"fullstack-engineer-observability-evals-platform-langsmith-7344fd30","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\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":"19f2bcb8-dfcf-45a4-b0a7-855bfd707cfa","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Engineering Manager","slug":"engineering-manager-3cdfd46c","description":"Overview\n\nInferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.\n\nAbout the Role\n\nWe're looking for an Engineering Manager to help scale a world-class, senior-heavy engineering organization working at the frontier of AI inference. You will lead a team of exceptional engineers across areas such as inference systems, performance, kernels, accelerators, and cloud infrastructure while creating the management capacity required for Inferact to keep growing quickly without sacrificing technical quality or execution speed.\n\nThis is a deeply technical management role. You will own team execution, hiring, coaching, prioritization, and operating rhythm, while staying close enough to the work to earn the trust of engineers solving hard systems problems. You will partner closely with engineering and company leadership to turn company priorities into clear technical direction, remove blockers, improve how the team works, and help every engineer do the highest-leverage work of their career.\n\n\n\nSkills and Qualifications\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, or similar.\n\n - Demonstrated experience managing strong software engineers in technically demanding systems, infrastructure, ML systems, distributed systems, or performance-oriented environments.\n\n - Strong technical credibility with the ability to understand architecture, ask useful questions, challenge assumptions, and help unblock senior engineers without becoming a bottleneck or overriding domain experts.\n\n - Experience owning the full people-management lifecycle, including hiring, onboarding, 1:1s, feedback, performance management, career development, team health, and retention.\n\n - Ability to translate ambiguous company priorities into clear team goals, sequencing, ownership, and execution plans while maintaining urgency in a fast-changing startup environment.\n\n - Track record of building effective engineering operating rhythms around planning, decision-making, technical reviews, cross-team coordination, and accountability without creating unnecessary process.\n\n - Strong judgment about when to dive into a technical problem, when to delegate, and how to create leverage for senior engineers rather than competing with them for ownership.\n\n - 2+ years of management experience with 10+ reports.\n\nPreferred qualifications:\n\n - Experience leading teams working on AI / ML infrastructure, model serving, distributed systems, GPU infrastructure, compilers, performance engineering, cloud infrastructure, or developer platforms.\n\n - Experience managing senior, staff, principal, research-adjacent, or PhD engineers in an environment where technical credibility and autonomy are especially important.\n\n - Experience scaling an engineering team through rapid hiring, including interviewer calibration, hiring decisions, onboarding systems, and thoughtful team design.\n\n - Familiarity with inference systems and adjacent technologies such as vLLM, SGLang, PyTorch, CUDA / Triton, Kubernetes, distributed serving, or accelerator infrastructure.\n\n - Experience partnering closely with founders or engineering executives in an early-stage company where priorities, team boundaries, and organizational needs evolve quickly.\n\nBonus points if you have:\n\n - Built or scaled a small, unusually high-talent engineering team where the technical surface area was specialized and the quality bar was extremely high.\n\n - Previously remained hands-on as an engineer or technical lead before moving into management and can still reason credibly about complex production systems.\n\n - Managed teams spanning multiple technical specialties, geographies, or time zones while preserving strong ownership and communication.\n\n - Helped establish engineering management practices from an early stage rather than inheriting a mature management system.\n\n - Contributed to open-source infrastructure, AI systems, developer tools, or technically ambitious products used by external engineers.\n\nLogistics\n\n - Location: This role is based in San Francisco, California and is in-office.\n\n - Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\n\n - Visa sponsorship: We sponsor visas on a case-by-case basis.\n\n - Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["cloud","llm","pytorch","gpu","mlops","distributed-systems","research"],"apply_url":"https://jobs.ashbyhq.com/inferact/98837c38-33a9-4085-97e7-1974dcf234a1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T11:23:31.216Z","expires_at":"2026-11-13T16:58:50.529662Z","created_at":"2026-10-14T15:22:31.292734Z","updated_at":"2026-10-14T16:58:50.533097Z","company_name":"Inferact","company_slug":"inferact","company_logo_url":"https://www.google.com/s2/favicons?domain=inferact.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/19f2bcb8-dfcf-45a4-b0a7-855bfd707cfa"},{"id":"12e81e2e-7f84-4b3e-8fc0-0b66e2fcdb45","company_id":"a0000000-0000-0000-0000-000000000001","title":"Engineering Manager, Claude Platform","slug":"engineering-manager-claude-platform-cc593300","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n Developers and enterprises build on Claude through the Claude Platform. Our models are getting exponentially more intelligent, and our APIs and developer tools need to evolve just as quickly so developers can take advantage of that intelligence safely.\n We're hiring Engineering Managers to lead teams that build these APIs and developer tools. We are shipping APIs and infrastructure for building and running agents in production. These APIs ship alongside SDKs, docs, and tooling that developers (and agents!) utilize in order to build high quality integrations. These abstractions are backed by our identity, verification, and trust platform, which lets us scale access to Claude responsibly.\n Key responsibilities\n \n Lead a team of engineers to ship high quality work quickly, owning planning, prioritization, delivery, and operational health\n Partner with product to set your team's roadmap, and bring a strong point of view on what developers need\n Talk to customers directly and turn what you learn into engineering priorities\n Guide API design and architecture decisions for distributed systems that have to stay reliable as usage grows\n Work closely with Research, Safeguards, Security, and go-to-market teams to bring new model capabilities to developers safely\n Recruit, coach, and grow strong engineers\n \n Minimum qualifications\n \n You have experience managing high-performing software engineering teams shipping developer products, including hiring, coaching, and performance management\n You're product-minded and customer-oriented. You care about who uses what your team builds, and you've shaped roadmaps alongside product partners\n You've built and scaled distributed systems in production, and you have the technical depth required to guide architecture and API design\n You've led teams that ship quickly and hold a high quality bar\n You collaborate well across functions and can align teams you don't manage\n You care about Anthropic's mission and about developing AI safely\n \n Preferred qualifications\n \n Experience building agent infrastructure or products on top of LLMs\n Experience with identity, verification, access control, or trust and safety systems\n \n Deadline to apply:  None. Applications will be reviewed on a rolling basis. \n Location Preference: Preference will be given to candidates based in NY, SEA, SF or the Bay Area given the current location of team. \n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $405,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before you","salary_min":405000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["api-design","distributed-systems","llm","alignment","platform"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5378643008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T04:15:36Z","expires_at":"2026-11-13T16:54:55.457822Z","created_at":"2026-10-14T15:17:21.754142Z","updated_at":"2026-10-14T16:54:55.461135Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/12e81e2e-7f84-4b3e-8fc0-0b66e2fcdb45"},{"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":"dde20c9f-561a-4f7f-bc17-cbce490cd4b4","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Legal Operations Tech and AI Enabler","slug":"legal-operations-tech-and-ai-enabler-d39c1c40","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n ABOUT THE TEAM\n The Legal Operations team is responsible for optimizing the efficiency and effectiveness of the Legal Department at Anduril and works cross-functionally to streamline workflows, automate routine tasks, implement best practices and support regulatory compliance efforts. With the overarching aim of supporting the Company’s overall strategic goals, the Legal Operations team acts as a bridge between legal, the Company’s business units, and outside counsel.\n ABOUT THE JOB\n We are looking for a Legal Operations Engineer to join our rapidly growing team on-site in Costa Mesa, California. This is a hands-on technical execution role where you’ll serve as the operational backbone for the Legal Department’s technology ecosystem for our fast-paced, multi-disciplinary team. You will spend real time in Jira, Airtable, Foundry, our CLM, and the seams between them. Expect to move between a same-day fix for a business partner who can't get a document routed, a data cleanup across a few thousand contract records, and a configuration change that shaves a step out of an approval chain. You will become a member of the team who knows how every one of these systems actually behaves, not just how it is supposed to work, which is the foundation for designing the next generation of them. As you build that fluency and demonstrate technical judgment, the work shifts from maintaining to building: scoping requirements, designing automated workflows, and eventually taking point on new system implementations as this function grows. If you are scrappy, technically curious, allergic to manual work a machine could do, and comfortable being the person who gets pinged when a workflow breaks, this role is for you.\n WHAT YOU'LL DO\n Systems Administration \u0026 Operational Excellence \n \n Integrate into the Legal Department's intake and support queue in Jira - triaging tickets, setting expectations, resolving requests, and escalating what needs a lawyer's judgment to gain first-hand knowledge of our fundamental workflows\n Serve as frontline administrator and first responder for our legal systems, including CLM, e-signature, matter management, document management, e-billing, and AI tooling - access, permissions, templates, configuration, and break/fix\n Monitor existing automations and integrations for failures, diagnose root causes, and fix them\n Execute recurring operational work: user provisioning and offboarding, template updates, approval routing changes, access reviews, and system hygiene checks\n Maintain data integrity and quality across contract and matter management systems - establishing metadata standards, field hygiene, bulk updates, deduplication, and cleanup ahead of reporting cycles\n Produce and refresh recurring reporting and dashboards on cycle times, request volume, contract throughput, and outside counsel spend\n \n Process Improvements \n \n Make incremental improvements to existing workflows: reduce manual steps, tighten validation, add conditional logic, eliminate the failure modes you keep seeing in the queue\n Build automations and integrations to close gaps between systems using no-code/low-code platforms, APIs, and scripting\n Track recurring pain points from the ticket queue and surface them to Legal Ops with a proposed fix and a rough sense of effort\n Assist with data migrations and configuration changes during larger Legal Ops initiatives\n Support AI tooling in the department - prompt and template maintenance, output quality spot-checks, and honest reporting on where the tool isn't working\n \n Growing into \n \n Driving tool adoption through training, office hours, and enablement materials - including the follow-up that turns a launched tool into one people actually use\n Scoping and designing new workflows and lightweight applications from ambiguous requirements, with increasing autonomy over time\n Translating what lawyers, paralegals and peer operations associates need into technical requirements, then building, documenting, and supporting the result\n Taking a lead technical role in new system implementations and vendor evaluations\n Partnering with IT, Security, Procurement, and Finance on integrations, vendor onboarding, data handling, and export control requirements\n Shaping the technical dir","salary_min":116000,"salary_max":154000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["llm","payments","api-design","computer-vision","legal"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5264938007?gh_jid=5264938007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T22:32:16Z","expires_at":"2026-11-13T16:57:15.832198Z","created_at":"2026-10-14T15:21:00.965332Z","updated_at":"2026-10-14T16:57:15.835205Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dde20c9f-561a-4f7f-bc17-cbce490cd4b4"},{"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":"2bdabd0b-9451-4388-b502-2ba7d839c43f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, Drive","slug":"senior-machine-learning-engineer-drive-202e134e","description":"About the Team\n DoorDash Drive powers deliveries placed through merchants' own channels—including their websites, mobile apps, and phone orders—using DoorDash's logistics network. The Drive Machine Learning team builds the prediction and intelligence systems that power this business, including delivery and pickup time estimation, merchant prep-time prediction, order release optimization, logistics decision-making, and AI-powered delivery quality signals. Drive presents a unique machine learning challenge. Every merchant has different operational workflows, preparation patterns, and customer expectations, requiring models that generalize across millions of deliveries while adapting to highly diverse merchant behavior. Our team has significant opportunities to improve prediction accuracy, optimize logistics decisions, and build AI-native experiences that directly improve merchant, consumer, and dasher outcomes.\n About the Role\n As a Machine Learning Engineer on the Drive team, you'll own machine learning systems end-to-end—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration. Your work will span several high-impact problem areas:\n \n Build next-generation machine learning models for delivery ETA, pickup ETA, merchant prep-time estimation, and order release prediction that improve reliability for merchants and consumers.\n Develop deep learning models that leverage large-scale spatiotemporal, marketplace, and behavioral signals to improve prediction accuracy.\n Apply reinforcement learning and optimization techniques to improve logistics decision-making, assignment strategies, and marketplace efficiency.\n Build AI-native product experiences using large language models (LLMs) and vision-language models (VLMs). For example, transform pickup photos, item verification flows, receipts, and drop-off images into structured quality signals that help verify orders, prevent delivery defects, and improve issue resolution.\n Design and run rigorous online experiments, production monitoring, and model iteration to continuously improve performance.\n Partner closely with software engineers, product managers, data scientists, and platform teams to bring new machine learning capabilities into production at scale.\n You'll have the opportunity to work across traditional machine learning, deep learning, reinforcement learning, optimization, and multimodal AI while solving some of the most challenging logistics problems at DoorDash.\n \n You're excited about this opportunity because... \n \n You enjoy solving large-scale machine learning problems that directly impact millions of deliveries.\n You have a strong sense of ownership and enjoy taking models from research through production.\n You're comfortable working in ambiguous environments where experimentation and iteration drive product decisions.\n You care about both model quality and production reliability.\n You're excited to work across a diverse set of ML techniques—from neural networks and optimization to multimodal AI.\n You collaborate well across engineering, product, and data science teams.\n \n We’re excited about you because… \n \n 5+ years of industry experience building and shipping production machine learning systems with measurable business impact (Bachelor's, Master's, or PhD).\n Strong experience developing production machine learning models using modern deep learning frameworks such as PyTorch and distributed data processing technologies such as Spark and Airflow.\n Experience building, deploying, monitoring, and maintaining production ML systems end-to-end.\n Strong software engineering skills in Python and experience with modern ML infrastructure and tooling.\n Deep expertise in at least one of the following areas:\n \n Deep Learning\n Reinforcement Learning\n Optimization / Operations Research\n Large Language Models (LLMs) or Vision-Language Models (VLMs)\n \n Experience applying machine learning to estimation, ranking, prediction, optimization, or decision-making problems at production scale.\n Hands-on experience with LLMs or VLMs is a strong plus.\n Experience in logistics, marketplaces, or delivery platforms is helpful but not required.\n Proficiency using AI-assisted development tools (e.g. Claude Code, Codex, Cursor) throughout the software development lifecycle.\n You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.\n Compensation \n The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions.  Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about yo","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","deep-learning","reinforcement-learning","cloud","llm","generative-ai","healthcare","pytorch"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8221807","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:42:26Z","expires_at":"2026-11-13T17:03:28.706911Z","created_at":"2026-10-14T15:25:31.350903Z","updated_at":"2026-10-14T17:03:28.710264Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2bdabd0b-9451-4388-b502-2ba7d839c43f"},{"id":"9b894a7b-e6ce-485c-b095-970a736af6dc","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, New Verticals Agentic Foundations","slug":"senior-machine-learning-engineer-new-verticals-agentic-foundations-a3f3a5d5","description":"About the Team\n Agentic Foundations, part of New Verticals ML, builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash, and proves it through flagship agent experiences such as the Ask grocery assistant. We operate as a closed loop: new models, evals, and representations make our agents better, and real-world agent usage surfaces the gaps that drive our next foundations work. \n About the Role\n We’re looking for a Senior Machine Learning Engineer to join Agentic Foundations with a primary focus on Vertical Agent Development. You will own the continuous improvement of our flagship agents, starting with the Ask Assistant, using an eval-driven loop to make them faster, more cost-efficient, and more reliable for millions of customers. You will also help us bring this approach to new agentic experiences for Merchants and Dashers, turning promising ideas into production-ready agents.\n This is a chance to work at the intersection of LLMs, agents, post-training, and evals on problems that ship. You will partner closely with engineers working on agentic memory, agent-compatible product representations, and post-training of small language models (SLMs), so that what we learn from production agents feeds directly back into our foundations, and what we build there makes our agents better.\n You will report into the engineering lead on our New Verticals AI/ML team. We expect this role to be hybrid with some time in-office and some time remote.\n You’re excited about this opportunity because you will…\n \n Raise the quality of our Consumer agents: build and refine our eval harness-optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context, and measure the impact on real customer tasks.\n Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can replace or augment larger LLMs.\n Launch new agents: take agentic opportunities for Merchants and Dashers from early exploration to production.\n Turn foundations into product impact: work with teammates on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation, and bring those capabilities into production agents.\n Shape the roadmap: partner with engineering, product, and business leaders to define an ML-driven strategy for our fast-growing grocery and retail delivery business.\n \n We’re excited about you because you have…\n \n 3+ years of industry ML experience, including hands-on work building and shipping LLM-based agents (tool use, context management, prompting, guardrails) and improving them with evals and data\n Experience with post-training or fine-tuning of open-weights models (e.g., SFT, preference optimization, or RL), ideally including small language models, and sound judgment on quality, latency, and cost trade-offs\n Strong foundation in NLP and machine learning, with proficiency in Python and frameworks such as PyTorch or TensorFlow\n M.S. or PhD in Computer Science, Statistics, Math, or another quantitative field, or equivalent practical experience, plus a collaborative, growth-minded approach and a drive for measurable impact\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United","salary_min":167800,"salary_max":246800,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","llm","fine-tuning","agents","cloud","pytorch","nlp","healthcare"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8279145","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:38:05Z","expires_at":"2026-11-13T17:03:28.711787Z","created_at":"2026-10-14T15:25:31.355292Z","updated_at":"2026-10-14T17:03:28.714838Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9b894a7b-e6ce-485c-b095-970a736af6dc"},{"id":"634e56a9-b38d-474b-847e-3dc417b38339","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Personalization","slug":"staff-machine-learning-engineer-personalization-d2d9e9a7","description":"About the Team \n Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop modern growth and personalization models that power DoorDash's growing retail and grocery business.\n About the Role \n We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning,  experience with solving end-user problems, and collaborate well with multi-disciplinary teams.\n You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).\n You’re excited about this opportunity because you will… \n \n Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space\n Partner with engineering and product leaders to help shape the product roadmap applying ML\n Mentor junior team members, and lead cross functional pods to create collective impact\n \n You can find out more on our ML blog here \n We’re excited about you because you have… \n \n 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. \n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field\n Expertise in applied ML for  Causal Inference and Recommendation Systems  -  both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms \u0026 LLMs is a plus.  \n Machine learning background in Python; experience with PyTorch or TensorFlow preferred.\n Ability to communicate technical details to nontechnical stakeholders\n You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down\n Desire for impact with a growth-minded and collaborative mindset\n \n \n Data Retention, Privacy \u0026 Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page . \n Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only\n We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.\n The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey \n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","cloud","deep-learning","fine-tuning","healthcare","pytorch","tensorflow","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8225006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:31:26Z","expires_at":"2026-11-13T17:03:28.796671Z","created_at":"2026-10-14T15:25:31.438732Z","updated_at":"2026-10-14T17:03:28.799759Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/634e56a9-b38d-474b-847e-3dc417b38339"},{"id":"501a4fae-853d-480b-9dbb-39daa44f8047","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Fulfillment Planning","slug":"staff-machine-learning-engineer-fulfillment-planning-53570b60","description":"About the Team \n The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability.  Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation.\n The team works on some of DoorDash’s most important logistics systems, including:\n \n The core assignment engine that matches deliveries with Dashers in real time.\n Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines.\n Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering.\n ML models and optimization algorithms that shape demand, improve service quality, and reduce cost.\n Tier-0 logistics services that require high reliability, low latency, and strong operational discipline.\n \n The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash.\n About the Role \n We’re looking for a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that drive real-time decisioning across DoorDash’s fulfillment ecosystem.\n You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines.\n This is a high-impact individual contributor role for someone who enjoys building 0→1 ML systems, operating at Staff-level scope, and influencing technical direction across multiple teams. You will define architectures, set modeling and deployment standards, mentor other engineers, and help shape how DoorDash applies machine learning to logistics at scale.\n You’re excited about this opportunity because you will… \n \n Own and build foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.\n Work on challenging, real-world machine learning problems , including real-time assignment, routing, and fulfillment estimation.\n Lead 0→1 ML initiatives , defining how machine learning and optimization are applied across fulfillment products.\n Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.\n Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.\n Establish best practices for model development, deployment, monitoring, retraining, and governance.\n Define and lead DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics\n Mentor other engineers and raise the technical bar for logistics ML across the organization.\n \n We’re excited about you because… \n \n You have 8+ years of industry experience building and deploying production-scale machine learning systems.\n You have strong machine learning fundamentals and know how to apply them to large-scale production systems.\n You are fluent in Python\n You have hands-on experience with modern ML frameworks, especially deep learning frameworks.\n You have designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.\n You can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.\n You communicate clearly with both technical and non-technical audiences.\n You are comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.\n You have built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.\n You have experience with knowledge distillation from large teacher models into efficient production models.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares ","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","healthcare","cloud","generative-ai","llm","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8224336","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:30:37Z","expires_at":"2026-11-13T17:03:28.79242Z","created_at":"2026-10-14T15:25:31.434336Z","updated_at":"2026-10-14T17:03:28.795093Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/501a4fae-853d-480b-9dbb-39daa44f8047"},{"id":"3fe52aee-b25e-4fe7-bb7c-574f48250345","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Staff Software Architect, Intelligence Systems ","slug":"staff-software-architect-intelligence-systems-715023cb","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n ABOUT THE TEAM\n Anduril Intelligence Systems (AIS) is focused on positioning Anduril as a lead provider of specialized engineering and next generation national security products for Intelligence Community (IC) customers. We work within the IC to understand their requirements, shape their concepts of operation, and deliver exquisite capability across their problem set. We aim to develop and deploy critically needed capabilities that address our customers’ most pressing national security requirements.\n ABOUT THE JOB\n We are looking for a Software Architect to join our rapidly growing team in Reston, VA. In this role, you will be responsible for defining the architectural direction of large-scale, real-time, mission-critical software. You will lead the design of high-performance systems spanning tactical edge deployments to distributed backend infrastructure, make critical trade-offs between performance, modularity, and maintainability at scale, and mentor senior engineers across the organization. This will require deep expertise in modern C++ and Rust, experience architecting numerics-heavy real-time systems, fluency with asynchronous/multithreaded programming (e.g., Tokio), and a strong grasp of the complex intersection of software and math — including state estimation, target tracking, optimization, and dynamic programming (MCP / Bellman equations) . If you are someone who thrives on ambitious problems, enjoys troubleshooting real-world systems, and wants to build software that is beyond a proof-of-concept — part of real tactical code deployed to the warfighter — then this role is for you. \n WHAT YOU’LL DO \n \n Define and drive the architectural vision for large-scale software systems that ingest, fuse, and act on data from diverse sensors in real time\n Lead the design and implementation of performant, real-time, embedded bare-metal systems in Rust at production scale\n Make high-leverage architectural trade-offs across performance, modularity, testability, and maintainability for mission-critical, edge-deployed systems\n Mentor and technically lead senior engineers, setting coding standards, review processes, and CI/CD best practices across the team\n Engage directly with customers to ensure successful outcomes for mission-critical needs\n Troubleshoot complex, real-world system issues spanning software, math assumptions, sensor behavior, and networking\n Contribute to all phases of the software development lifecycle including prototyping, modeling \u0026 simulation, field testing, and deployment\n Help shape hiring and technical growth of the broader organization as it hyper scales\n \n REQUIRED QUALIFICATIONS\n \n 10+ years of software engineering experience with a Bachelor's degree (or equivalent) in Computer Science, Applied/Computational Mathematics, Electrical Engineering, Aerospace Engineering, Controls/Dynamical Systems, Statistics, or related field\n Proven experience architecting large-scale, production-grade codebases in real-time or high-performance environments\n Experience with modern IPC frameworks and fundamentals\n Experience architecting and designing REST APIs for public consumption to articulate complex real-time systems\n Experience with CI/CD, unit testing, git version control, and microservices\n Currently possesses and is able to maintain an active U.S. Secret clearance.\n \n PREFERRED QUALIFICATIONS\n \n M.S. or Ph.D. in a technical field (dual academic background in software + math highly valued)\n Domain expertise in embedded bare-metal systems\n Expert-level proficiency in modern Rust, including asynchronous and multithreaded programming (e.g., Tokio for Rust)\n Experience with FPGA\n Deep knowledge of networking protocols and systems\n Expertise or experience in cryptographic algorithms and the fundamentals of both symmetric and asymmetric cryptography\n Prior DoD program experience (Army, Air Force, MDA, SCO, Navy)\n Experience with GPU / CUDA kernel development\n \n  \n  \n US Salary Range\n $220,000 — $292,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considera","salary_min":220000,"salary_max":292000,"location":"Reston, VA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["cloud","computer-vision","payments","gpu","llm","microservices","api-design"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5261353007?gh_jid=5261353007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:16:39Z","expires_at":"2026-11-13T16:57:16.705084Z","created_at":"2026-10-14T15:21:01.965044Z","updated_at":"2026-10-14T16:57:16.707891Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3fe52aee-b25e-4fe7-bb7c-574f48250345"},{"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"}],"page":1,"per_page":20,"total":2724,"total_is_exact":true,"total_pages":137}