{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\n\n\nABOUT THE ROLE\n\n We're looking for a Fullstack Engineer to work on LangSmith, our commercial AI observability and evals platform product.  In this role, you'll have the opportunity to build new features and functionality for the platform while working close enterprise customers, developer end-users and internal stakeholders.\n\n\n\nWHAT YOU WILL DO\n\n - Develop and implement key product features working across our entire Go, Python, and Typescript stack\n\n - Collaborate closely with product and design teams to iterate on new features and enhance the product roadmap\n\n - Drive aggressive project timelines while ensuring high engineering standards are being met by writing clean, maintainable, and well-tested code\n\n\n\n\nHOW TO BE SUCCESSFUL IN THIS ROLE\n\n - 2+ years of experience in software engineering working on complex platform products\n\n - Fullstack engineering experience with Go or Python on the backend and React + Typescript on the frontend\n\n - Solid understanding of database systems, particularly Postgres and Redis\n\n - Experience designing and scaling APIs, preferably in high-performance, data-intensive applications\n\n - Strong ownership and a “get stuff done” mindset - comfortable shipping fast and iterating based on user feedback\n\n - Strong communicator with the ability to collaborate closely with various internal stakeholders and engage directly with our top-tier customers\n\n - Bonus: Experience with AI/LLM-powered applications, developer tools, or observability platforms\n\n\n\nCompensation\nAnnual salary range: $145,000-180,000 USD \n\nCompensation Philosophy:\n\nWe offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. \n\nWe are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.\n\n\n\n\nBENEFITS\n\nBenefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.","salary_min":145000,"salary_max":180000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["llm","agents","fullstack","evaluation"],"apply_url":"https://jobs.ashbyhq.com/langchain/ddf92275-1cc3-49c0-9f25-e8ded43b07f6/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T15:18:56.705Z","expires_at":"2026-11-13T16:55:07.883661Z","created_at":"2026-04-13T09:37:32.897623Z","updated_at":"2026-10-14T16:55:07.886796Z","company_name":"LangChain","company_slug":"langchain","company_logo_url":"https://www.google.com/s2/favicons?domain=langchain.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a3456dd3-15e8-4fdc-8daf-74e40b0d4955"},{"id":"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":"26b23c3e-0157-450b-895d-33b58f6ed9e9","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Senior Fullstack Software Engineer, AI Observability \u0026 Evals Platform (NY)","slug":"senior-fullstack-software-engineer-ai-observability-evals-platform-ny-2f6cc19e","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE:\n\nWe're looking for a Senior Fullstack Engineer for our commercial product LangSmith, an observability and evals platform.  In this role, you'll have the opportunity to shape the technical direction of our platform while working closely with enterprise customers, developer end-users and internal stakeholders.\n\n\n\nWHAT YOU WILL DO:\n\n - Lead the technical architecture and implementation of key product features for LangSmith, working across our entire Go, Python, and Typescript stack.\n\n - Collaborate closely with product and design teams to iterate on new features.\n\n - Mentor and guide more junior members of the team. Drive aggressive project timelines while ensuring high engineering standards are being met.\n\n - Lead by example in writing clean, maintainable, and well-tested code\n\n \n\n\nWHAT YOU BRING:\n\n - 5+ 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 - Strong understanding of database systems, particularly Postgres and Redis\n\n - Experience designing and scaling APIs, preferably in a high-performance, data-intensive application\n\n - A “get stuff done” attitude - 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 - Strong ownership mindset\n\n - Bonus: Experience with AI/LLM-powered applications, developer tools, or observability platforms\n   \n    \n\nAnnual salary range:  $175,000-$240,000 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":175000,"salary_max":240000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","evaluation","fullstack"],"apply_url":"https://jobs.ashbyhq.com/langchain/4beade3b-0bf2-4720-b553-0309880e22b4/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T04:05:32.87Z","expires_at":"2026-11-13T16:55:08.148698Z","created_at":"2026-10-14T15:18:57.779269Z","updated_at":"2026-10-14T16:55:08.152669Z","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/26b23c3e-0157-450b-895d-33b58f6ed9e9"},{"id":"83c4050f-83e8-4b87-965f-69179cf623b4","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Senior Backend Software Engineer, AI Observability \u0026 Evals Platform (NYC)  ","slug":"senior-backend-software-engineer-ai-observability-evals-platform-nyc-bd87c734","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE TEAM\n\nThe LangSmith team owns and builds LangChain's core platform for observability, evaluation, and production reliability of AI systems. From tracing and annotation to run rules, evaluations, and beyond, this team owns LangSmith end-to-end. If you want to define what great AI observability looks like at production scale, this is where that work gets done.\n\n \n\n\nABOUT THE ROLE\n\nWe are looking for a Senior Backend Engineer to join us. In this role you will be building the backend systems that power LangChain’s observability and evals platform. You will work on the core services that allow developers to monitor and evaluate their AI applications at scale. While the focus is on backend feature development, familiarity with fullstack or frontend engineering, performance tuning, and debugging production issues would be extremely valuable.\n\n \n\n\nWHAT YOU'LL DO\n\n - Design, develop, and maintain backend services and APIs to support LangSmith’s tracing, monitoring, and evaluation workflows.\n\n - Collaborate on architectural decisions to ensure systems are performant and maintainable.\n\n - Optimize storage and query performance for high-volume observability and evaluation data.\n\n - Ensure system reliability through strong testing, monitoring, and alerting practices.\n\n - Troubleshoot and resolve production issues, performing root-cause analysis and implementing long-term fixes.\n\n - Create and maintain technical documentation, including system design and API references.\n\n - Work closely with full-stack engineers, product managers, and product designers on rolling out new features.\n   \n    \n\n\nWHAT YOU'LL BRING\n\n - 5+ years of professional experience in backend engineering working on highly complex products\n\n - Proficiency in one or more backend languages/frameworks, ideally Python or Go\n\n - Strong understanding of API design and building reliable data services.\n\n - Experience with high-throughput or mission-critical systems.\n\n - Demonstrated ability to optimize backend services for performance and reliability.\n\n - Experience with database systems (Postgres, Redis, Clickhouse), and cloud platforms (AWS, GCP, Azure)\n\n - Strong communication skills, with the ability to collaborate cross-functionally\n\n - Customer centricity and ownership mentality\n   \n    \n\nCompensation \n\nAnnual salary range: $180,000- $240,000\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":180000,"salary_max":240000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["api-design","agents","evaluation","backend"],"apply_url":"https://jobs.ashbyhq.com/langchain/20189c9d-f1af-4385-ae06-c3a6c7681ff0/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T04:01:55.653Z","expires_at":"2026-11-13T16:55:08.144623Z","created_at":"2026-10-14T15:18:57.775711Z","updated_at":"2026-10-14T16:55:08.147281Z","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/83c4050f-83e8-4b87-965f-69179cf623b4"},{"id":"231dd3ff-7e25-4bb7-9a76-3cb68476e1a8","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Evaluation Infrastructure","slug":"member-of-technical-staff-evaluation-infrastructure-eeccc78b","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\n ABOUT THE ROLE: \n The RL infrastructure team is looking for an engineer to help develop our evaluation infrastructure.\n RESPONSIBILITIES: \n \n Build highly reliable, efficient, and easy to use infrastructure that runs all our evaluations at scale\n Collaborate closely with modeling \u0026 evaluation teams on developing new evals, maintaining evaluation signal quality, and building internal tooling to support training our next-generation of models\n Identify and resolve performance bottlenecks in all layers of the eval infrastructure stack, including inference, capacity fleet management, asynchronous eval orchestration, and more\n \n BASIC QUALIFICATIONS: \n \n Experience in building, debugging, and optimizing efficiency of large-scale distributed systems\n Willingness to dive deep and solve hard problems at all levels of the stack\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Experience in LLM inference\n Experience in GPU compute management, workload scheduling, and dynamic resource optimization\n Experience in developing interfaces for comparing models and evals\n \n COMPENSATION AND BENEFITS: \n $180,000 - $440,000 USD \n Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks. \n SpaceXSI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","distributed-systems","infrastructure","evaluation"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5252714007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T19:41:34Z","expires_at":"2026-11-13T16:55:31.67405Z","created_at":"2026-10-14T15:19:21.609414Z","updated_at":"2026-10-14T16:55:31.676983Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/231dd3ff-7e25-4bb7-9a76-3cb68476e1a8"},{"id":"2d20090b-e536-4e32-8e91-f25675ac4950","company_id":"92df3417-f362-4f1a-9406-e34d8013b283","title":"AI Evaluation Infrastructure Engineer","slug":"ai-evaluation-infrastructure-engineer-4ec228d5","description":"It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible. Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.\n The Role \n We build AI products, and the quality of our evaluations sets the ceiling for how good those products can be. The speed of our evaluations determines how quickly we can improve them.\n We are looking for an engineer to build the infrastructure and tooling that make high-quality AI evaluation possible at Block's scale. You will help teams understand whether a model or product change is actually better, whether a result is statistically meaningful, and whether offline evaluation is predicting what happens with real users.\n Our evaluation approach combines offline evals that encode our definition of a good response, online evals that show how people actually respond, and a feedback loop that keeps the two converging. Your work will turn that approach into systems that product teams can use quickly, reliably, and with confidence.\n This is a high-impact, early-stage area with broad surface area. You will help decide what to build first, then build the platform that helps teams ship better AI products faster.\n You Will \n \n Build an execution engine that can score candidate versions against task sets in minutes, not hours.\n Create task set tooling that samples from production logs and validates tasks before they are admitted into an evaluation set.\n Build grader infrastructure across ground truth checks, rubrics, and LLM-as-judge approaches.\n Develop tooling that helps human reviewers calibrate judges, measure judge-to-human agreement, and monitor drift over time.\n Build leaderboards and reporting systems that include sample size, confidence intervals, and run-to-run variance, so teams can distinguish real improvements from noise.\n Support in-product side-by-side serving, feedback capture, and implicit signal extraction from real conversations.\n Build the loop that compares offline scores with online outcomes, identifies eval sets that have stopped predicting reality, and helps teams improve them.\n Partner with product, engineering, data, and ML teams to make evaluation workflows fast enough and trustworthy enough to become part of everyday development.\n \n You Have \n \n Experience building production platforms or infrastructure, including distributed batch execution, data pipelines, or systems that process production logs.\n Strong statistical literacy, including comfort with confidence intervals, variance, power, and multiple comparisons.\n The judgment to identify when a result is meaningful and when it is noise.\n Experience evaluating LLM or ML systems, or deep systems engineering experience with a strong interest in AI evaluation.\n Product instinct for internal tools. You understand that leaderboards, annotation tools, and workflows only matter if teams actually use them.\n A bias toward building reliable, observable systems that other engineers can trust.\n Strong collaboration skills and the ability to work across ambiguous product, data, and engineering problems.\n \n Success in your first year \n \n Product teams can stand up credible evals for new AI surfaces in days.\n Eval results gate CI and run quickly enough that engineers do not route around them.\n Judge-to-human agreement is measured, published, and monitored for drift.\n Launch decisions are not made on results that are within statistical noise.\n At least one case is documented where online reality disagreed with offline scores, and the evaluation was improved as a result.\n \n Why this matters \n AI product development moves quickly, but speed only helps when teams can trust the signal they are using to make decisions. This role will build the systems that make those signals faster, more accurate, and more actionable. Your work will directly influence how Block evaluates, improves, and ships AI products.\n  \n Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditio","salary_min":263600,"salary_max":395400,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","data-pipeline","payments","data-science","infrastructure","evaluation"],"apply_url":"http://block.xyz/careers/jobs/5434157008?gh_jid=5434157008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T18:33:32Z","expires_at":"2026-11-13T16:58:11.19989Z","created_at":"2026-10-14T15:21:51.219903Z","updated_at":"2026-10-14T16:58:11.202872Z","company_name":"Block","company_slug":"block","company_logo_url":"https://www.google.com/s2/favicons?domain=block.xyz\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2d20090b-e536-4e32-8e91-f25675ac4950"},{"id":"2985c23c-d3fc-4cc6-b281-627448282e86","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Engineer, Retrieval","slug":"staff-machine-learning-engineer-retrieval-05eab34b","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Team Description: \n The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies. Our work combines representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation.\n Role Description: \n We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers. \n Responsibilities: \n \n Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders.\n Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit’s advertising surfaces.\n Apply modern approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques when they create meaningful product value.\n Improve the retrieval stack across key modeling decisions, including objectives, labels, sampling strategies, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth.\n Work with approximate nearest-neighbor and vector retrieval systems, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs.\n Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes.\n Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration.\n Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel.\n Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership.\n Mentor ML engineers and help grow the team’s expertise in retrieval, recommendation, and representation learning.\n \n Required Qualifications: \n \n 7+ years of industry experience, including substantial experience building and shipping applied ML products.\n Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems.\n Strong understanding of retrieval modeling concepts, including DNN, embeddings, two-tower or dual-encoder models, approximate nearest-neighbor search, and multi-stage retrieval.\n Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks.\n Demonstrated ownership of ML projects from problem framing and data preparation through offline evaluation, online experimentation, production launch, and iteration.\n Strong command of experimental design and model evaluation, including how offline retrieval metrics relate to downstream business and user metrics.\n Experience working with large-scale behavioral, contextual, or content datasets and complex feature pipelines.\n Strong software engineering fundamentals and the ability to write clear, reliable, maintainable production code.\n Technical leadership experience: setting direction, leading complex projects, influencing partner teams, and mentoring other engineers.\n Excellent written and verbal communication, with the ability to explain complex modeling choices to technical and non-technical audiences.\n \n Preferred Qualifications: \n \n Experience with ads retrieval, ad serving, recommendation, search relevance, or marketplace optimization\n Experience modeling user, content, campaign, or ad interactions with sequential, graph, or multimodal signals\n Experience connecting retrieval improvements to downstream rank","salary_min":230000,"salary_max":322000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","search","pytorch","tensorflow","healthcare","machine-learning","evaluation"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8178085","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-16T22:20:41Z","expires_at":"2026-11-13T16:57:52.354441Z","created_at":"2026-10-14T15:21:32.515721Z","updated_at":"2026-10-14T16:57:52.357632Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2985c23c-d3fc-4cc6-b281-627448282e86"},{"id":"93af39ef-240a-4126-b57b-8991a6b57ec0","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Senior Fullstack Engineer,  AI Observability \u0026 Evals Platform ","slug":"senior-fullstack-engineer-ai-observability-evals-platform-d592235b","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE TEAM\n\nThe LangSmith team owns and builds LangChain's core platform for observability, evaluation, and production reliability of AI systems. From tracing and annotation to run rules, evaluations, and beyond, this team owns LangSmith end-to-end. If you want to define what great AI observability looks like at production scale, this is where that work gets done.\n\n\n\n\nABOUT THE ROLE\n\nThis role is onsite 5 days/week in Boston, MA\n\nWe're looking for a Senior Fullstack Engineer to work on LangSmith.  In this role, you'll have the opportunity to shape the technical direction of our platform while working closely with our enterprise customers, developer end-users, and cross functional internal stakeholders.\n\n\n\nWHAT YOU WILL DO\n\n - Lead the technical architecture and implementation of key product features for LangSmith, working across our entire Go, Python, and Typescript stack.\n\n - Collaborate closely with product and design teams to iterate on new features.\n\n - Help drive aggressive project timelines while ensuring high engineering standards are being met.\n\n - Lead by example in writing clean, maintainable, and well-tested code.\n\n\n\n\nWHAT YOU BRING\n\n - 5+ 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 - Strong understanding of database systems, particularly Postgres and Redis\n\n - Experience designing and scaling APIs, preferably in a high-performance, data-intensive application\n\n - A “get stuff done” attitude - 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 - Strong ownership mindset\n\n - Bonus: Experience with AI/LLM-powered applications, developer tools, or observability platforms\n   \n   \n\nCompensation \n\n - Annual salary range: $175,000-$240,000 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":175000,"salary_max":240000,"location":"Boston, MA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","evaluation","fullstack"],"apply_url":"https://jobs.ashbyhq.com/langchain/e6d2bc1e-e0b9-4076-be01-ef8681863c01/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-12T07:06:57.811Z","expires_at":"2026-11-13T16:55:08.122856Z","created_at":"2026-09-12T13:32:03.877289Z","updated_at":"2026-10-14T16:55:08.126382Z","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/93af39ef-240a-4126-b57b-8991a6b57ec0"},{"id":"17b25ba1-c1cd-4cbc-bd8a-cfd01e10125e","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Senior Backend Software Engineer, AI Observability \u0026 Evals Platform (LangSmith)","slug":"senior-backend-software-engineer-ai-observability-evals-platform-langsmith-6a1c13ed","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE TEAM\n\nThe LangSmith team owns and builds LangChain's core platform for observability, evaluation, and production reliability of AI systems. From tracing and annotation to run rules, evaluations, and beyond, this team owns LangSmith end-to-end. If you want to define what great AI observability looks like at production scale, this is where that work gets done.\n\n\n\n\nABOUT THE ROLE\n\nThis role is based onsite in Boston, MA.  \n\n\nWe are looking for a Senior Backend Engineer to join our growing engineering team in Boston.  In this role you will be building the backend systems that power LangSmith, LangChain’s observability and evals platform. You will work on the core services that allow our customer's developer teams to monitor and evaluate their AI applications at scale. While the focus is on backend feature development, familiarity with fullstack or frontend engineering, performance tuning, and debugging production issues would be valuable.\n\n\n\n\nWHAT YOU'LL DO:\n\n - Design, develop, and maintain backend services and APIs to support LangSmith’s tracing, monitoring, and evaluation workflows.\n\n - Collaborate on architectural decisions to ensure systems are performant and maintainable.\n\n - Optimize storage and query performance for high-volume observability and evaluation data.\n\n - Ensure system reliability through strong testing, monitoring, and alerting practices.\n\n - Troubleshoot and resolve production issues, performing root-cause analysis and implementing long-term fixes.\n\n - Create and maintain technical documentation, including system design and API references.\n\n - Work closely with full-stack engineers, product managers, and product designers on rolling out new features.\n   \n   \n\n\nWHAT YOU'LL BRING:\n\n - 4+ years of professional experience in backend engineering working on highly complex products\n\n - Proficiency in one or more backend languages/frameworks, ideally Python and/or Go\n\n - Strong understanding of API design and building reliable data services.\n\n - Experience with high-throughput, mission-critical systems.\n\n - Demonstrated ability to optimize backend services for performance and reliability.\n\n - Experience with database systems (Postgres, Redis, Clickhouse), and cloud platforms (AWS, GCP, Azure) would be a bonus.\n\n - Strong communication skills, with the ability to collaborate cross-functionally.\n\n - Customer centricity and ownership mentality\n\n\n\nAnnual salary range: $170,000- $235,000\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":170000,"salary_max":235000,"location":"Boston, MA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","api-design","backend","evaluation"],"apply_url":"https://jobs.ashbyhq.com/langchain/b23ea9af-c6dc-40fa-86ce-75042b905173/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-12T07:00:25.742Z","expires_at":"2026-11-13T16:55:08.118898Z","created_at":"2026-09-12T13:32:04.187229Z","updated_at":"2026-10-14T16:55:08.121687Z","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/17b25ba1-c1cd-4cbc-bd8a-cfd01e10125e"},{"id":"57d1cd4b-d690-4874-87f3-9545adfb386d","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Software Engineering Manager, AI Observability \u0026 Evals Platform","slug":"software-engineering-manager-ai-observability-evals-platform-1266eca9","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE TEAM\n\nThe LangSmith team owns and builds LangChain's core platform for observability, evaluation, and production reliability of AI systems. From tracing and annotation to run rules, evaluations, and beyond, this team owns LangSmith end-to-end. If you want to define what great AI observability looks like at production scale, this is where that work gets done.\n\n \n\n\nABOUT THE ROLE\n\nWe’re looking for an Engineering Manager to lead the team building LangSmith, our observability and evaluation platform for LLM applications. You’ll define the technical direction, grow and mentor a world-class engineering team, and partner with product and design to deliver features that help developers build and deploy trustworthy AI systems with confidence.\n\n \n\n\nWHAT YOU'LL DO\n\n - Build, mentor, and grow a high-performing team of engineers, fostering collaboration, ownership, and accountability\n\n - Strengthen LangChain’s engineering culture through mentorship, high-quality code, and technical excellence\n\n - Shape long-term technical direction and ensure scalability and reliability of LangSmith AI Observability Platform\n\n - Partner with product and design teams to define scope, sequence, and success criteria for key initiatives\n\n - Maintain a high bar for technical excellence while making sure the team moves with urgency and focus\n\n - Lead by example in writing clean, maintainable, and well-tested code in Go/Python and Typescript\n\n - Engage directly with customers to understand their needs and translate them into actionable product improvements\n\n \n\n\nWHAT YOU'LL BRING\n\n - Experience managing or tech-leading teams delivering complex full-stack products\n\n - Strong technical background in Go/Python and React/Typescript\n\n - Solid understanding of databases (Postgres, Redis and ClickHouse)\n\n - Proven ability to design high throughput data-intensive systems\n\n - Bias towards shipping quickly and learning from customers\n\n - Strong communication skills—able to collaborate with internal stakeholders and engage directly with customers\n\n - Bonus: Experience building AI/LLM-powered applications, developer tools, or observability platforms\n\n \n\nAnnual salary range: $220,000- $270,000\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":220000,"salary_max":270000,"location":"Boston, MA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","evaluation","platform"],"apply_url":"https://jobs.ashbyhq.com/langchain/f0396971-39fa-4d40-9af8-60e529bb6c86/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-11T03:09:29.305Z","expires_at":"2026-11-13T16:55:08.128321Z","created_at":"2026-09-12T13:32:07.979336Z","updated_at":"2026-10-14T16:55:08.131284Z","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/57d1cd4b-d690-4874-87f3-9545adfb386d"},{"id":"907ac14b-4fcc-48ee-8292-d453378d4b94","company_id":"a0000000-0000-0000-0000-000000000001","title":"Cyber Evaluations Engineer","slug":"cyber-evaluations-engineer-5351d4ad","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 We're hiring Cyber Evaluations Engineers to build and run the evaluations that measure cyber-relevant capabilities and safeguard robustness in our models. You'll design new evals, run per-release robustness testing, and dig into data on jailbreaks and prompt bypasses to understand where our safeguards hold up and where they don't. You'll also design many of the probes that detect cyber abuse in production and help shape the overall detection architecture alongside the policy team. \n Key responsibilities \n \n \n Design and run capability, uplift, and safety evaluations to assess cyber-relevant risk in new models\n \n Execute per-release safeguard-robustness testing ahead of major model launches\n \n Analyze evaluation results and communicate findings clearly to the team and to stakeholders\n \n Design, prototype, and tune detection probes for cyber misuse\n \n Work with the cyber policy team to turn policy lines into a layered, robust abuse-detection architecture, and measure its precision and coverage over time\n \n Build and maintain internal tooling used to run and score evaluations\n \n Collaborate with policy and engineering partners to translate eval findings into safeguard improvements\n \n Minimum qualifications \n \n \n Experience building or running evaluations, benchmarks, or test suites for software or ML systems, including delivering results on short, fixed timelines\n \n Hands-on cybersecurity experience (e.g., CTF participation, vulnerability research, exploit development, or security research)\n \n Proficiency in Python\n \n Strong ability to communicate evaluation results with multiple cross-functional stakeholders or potential policy stakeholders\n \n Preferred qualifications \n \n \n Deep offensive-security or security-research experience, including experience building AI security benchmarks\n \n Experience analyzing adversarial or abuse data (e.g., jailbreaks, prompt bypasses, intrusion or fraud telemetry)\n \n Experience working onsite with government partners on testing or evaluation engagements\n \n Experience with AI/ML evaluation frameworks\n \n Familiarity with coordinated vulnerability disclosure practices\n \n Experience testing pre-release or pre-deployment software or models under confidentiality constraints\n \n Experience authoring detection content (e.g., Sigma, YARA, Suricata, SIEM rules) or building ML-based abuse detection\n \n Active secret security clearance or higher, or eligibility to obtain one\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 $300,000 — $405,000 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,","salary_min":300000,"salary_max":405000,"location":"Washington, DC","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["security","alignment","evaluation","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5406367008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-01T21:26:36Z","expires_at":"2026-11-13T16:54:55.320955Z","created_at":"2026-09-02T13:30:16.806162Z","updated_at":"2026-10-14T16:54:55.323671Z","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/907ac14b-4fcc-48ee-8292-d453378d4b94"},{"id":"eaacbad1-cf06-4872-92b7-f9c1caa6a5fd","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Senior Frontend Engineer, AI Observability \u0026 Evals Platform ","slug":"senior-frontend-engineer-ai-observability-evals-platform-ffb86ec5","description":"ABOUT US\n\n\n\nAt LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.\n\nWith $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.\n\nToday, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.\n\n\n\n\n\n\nABOUT THE ROLE:\n\nThis position can be based remotely in \n\n\n\nWe’re looking to bring on an experienced frontend engineer to develop and enhance new features on LangSmith, our enterprise platform product for LLM application observability, testing, and debugging.  \n\n\n\n\nWHAT YOU WILL DO\n\n - Develop new user-facing features using React \u0026 Typescript\n\n - Build reusable components and front-end libraries for future use\n\n - Translate designs and wireframes into high-quality code\n\n - Optimize components for maximum performance across a vast array of web-capable devices and browsers\n\n - Collaborate with fullstack and backend developers and UX/UI designers, to enhance usability\n   \n   \n\n\nWHAT YOU WILL BRING\n\n - Extensive front-end engineering experience with strong proficiency in React, JavaScript and TypeScript\n\n - Hands-on experience with front-end development tools like Babel, Vite, Webpack, NPM, and Yarn\n\n - Familiarity with REST APIs and experience working closely with fullstack and backend engineers\n\n - Knowledge of modern authorization mechanisms, such as JWTs\n\n - A passion for user experience, design and building clean, engaging, and intuitive interfaces\n\n - Strong written and oral communication skills, with the ability to explain technical concepts clearly and concisely to both technical and non-technical stakeholders\n\n - The DNA to thrive in a start-up, fast-paced environment.  You view unstructured environments as an opportunity to make the most impactful work and to help define what's next.\n\n - An ownership mind-set. You are self-driven and look for opportunities to make an impact.\n\n - Strong computer science fundamentals, ideally with a degree in computer science or related field.  \n\n\n\nCompensation \n\nAnnual salary range: $180,000-$240,000 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":180000,"salary_max":240000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["api-design","llm","agents","evaluation","frontend"],"apply_url":"https://jobs.ashbyhq.com/langchain/afb91b9b-46d5-4c9d-aa84-a4f1a3f74263/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-13T18:33:10.038Z","expires_at":"2026-11-13T16:55:07.869986Z","created_at":"2026-04-13T09:37:32.570304Z","updated_at":"2026-10-14T16:55:07.872809Z","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/eaacbad1-cf06-4872-92b7-f9c1caa6a5fd"},{"id":"258e08a5-3f97-4f5e-bfaf-1bcd3e0ebd55","company_id":"10c1ac82-83d5-423a-b438-4cc7b13d597c","title":"Frontend Engineer, AI Observability \u0026 Evals Platform ","slug":"frontend-engineer-ai-observability-evals-platform-c60f6f55","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\nAbout The Role: \n\nWe’re looking to add a frontend engineer to develop and enhance new features on LangSmith, working on our enterprise platform product for LLM application observability, testing, and debugging.  \n\n*This role can be based in our San Francisco, New York, or Boston office. Employees within commuting distance work from the office five days per week. Candidates who live outside commuting distance (e.g. \u003e1hr each way), may be eligible for hybrid arrangements depending on location and role requirements.\n\n\nWhat You Will Do:\n\n - Develop new user-facing features using React \u0026 Typescript\n\n - Build reusable components and front-end libraries for future use\n\n - Translate designs and wireframes into high-quality code\n\n - Optimize components for maximum performance across a vast array of web-capable devices and browsers\n\n - Collaborate with fullstack and backend developers and UX/UI designers, to enhance usability\n\n\n\nWhat You Will Bring:\n\n - 2+ years of Front-end engineering experience with strong proficiency in React, JavaScript and TypeScript\n\n - Hands-on experience with front-end development tools like Babel, Vite, Webpack, NPM, and Yarn\n\n - Familiarity with REST APIs and experience working closely with fullstack and backend engineers\n\n - Knowledge of modern authorization mechanisms, such as JWTs\n\n - A passion for user experience, design and building clean, engaging, and intuitive interfaces\n\n - Strong written and oral communication skills, with the ability to explain technical concepts clearly and concisely to both technical and non-technical stakeholders\n\n - The DNA to thrive in a start-up, fast-paced environment. Views unstructured environments as an opportunity to figure out the most impactful work and help define the future success of the company\n\n - An ownership mind-set. You are self-driven and look for opportunities to make an impact.\n\n - Strong computer science fundamentals, ideally with a degree in computer science or related field\n\n\n\nCompensation\n\n - Annual salary range: $160,000-$195,000 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":160000,"salary_max":195000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["llm","agents","api-design","frontend","evaluation"],"apply_url":"https://jobs.ashbyhq.com/langchain/f7de4819-e7aa-4dfb-9acd-8b81ad8caf2c/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T01:29:10.739Z","expires_at":"2026-11-13T16:55:07.888458Z","created_at":"2026-08-25T18:26:42.681499Z","updated_at":"2026-10-14T16:55:07.891227Z","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/258e08a5-3f97-4f5e-bfaf-1bcd3e0ebd55"},{"id":"a8bfa422-da52-4371-aecb-c64631f4aaa0","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Senior Product Operations Manager, Evaluation Quality","slug":"senior-product-operations-manager-evaluation-quality-cc54ed93","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nWe’re looking for a senior operator to own the quality bar behind Harvey’s human evaluations. As we scale globally, the volume of eval work is growing 10x, but volume only matters if the output is trusted. This role makes Human Data’s signal decision-grade: rigorous, calibrated, and reproducible enough that Product, Engineering, and AI Research act on it to ship.\n\n\n\nAs a member of our Evaluation Operations team, you’ll work alongside our Evaluation Operations Manager (who runs throughput and coordination) and partner closely with Applied Legal Researchers, Product, Engineering, and AI Research. You'll set the standard for what \"good\" looks like across eval data and methodology, and own the data analyses to make conclusions that teams will rely on, building the stakeholder trust that lets EPD act on the signal.\n\n\n\n\nWHAT YOU'LL DO\n\n - Own the quality bar for Harvey’s human evaluations: define what “good” looks like for eval methodology and data analysis, and produce decision-grade outputs for EPD\n\n - Author and maintain the evaluation guidelines, instructions, and databases that contract attorneys work from\n\n - Standardize and streamline rubric and evaluation design into repeatable templates and one documented methodology, partnering with Applied Legal Research (ALR), who supplies feature-specific legal depth\n\n - Own contract-attorney quality: onboarding, calibration training, inter-rater reliability, and the feedback loop (including benchmarks and gold references) that keeps judgment consistent across attorneys and over time\n\n - Conducting quantitative and qualitative statistical analyses, diagnosing error states, investigating root causes, and turning raw eval results into a structured, prioritized signal Product and ALR can act on\n\n - Run QA on vendor and contract-attorney deliverables against a defined bar before results inform a launch decision\n\n - Ensure the quality bar holds across jurisdictions and non-English geographies as coverage expands\n\n - Establish one standard, documented way to analyze eval results, and build lightweight operational dashboards to track rater capability and eval-program health\n\n - Support ALR in a review step that certifies an evaluation is sound before it scales to contract attorneys\n\n - Partner with ALR and Analytics to determine where human eval aligns with online signal and where it can provide expanded insights\n\n\n\n\nWHAT YOU HAVE\n\n - 6+ years in product operations, research operations, evaluation/QA operations, or quality program management\n\n - A track record of owning quality inputs (guidelines, instructions, benchmarks, QA procedures) for complex, expert-driven or human-in-the-loop work\n\n - Experience onboarding, training, and calibrating a distributed pool of expert raters, annotators, or reviewers, and running the feedback loop that improves their quality over time\n\n - Enough grounding in measurement concepts (calibration, inter-rater reliability, sampling, rubric design) to independently set up and own the quality of our evaluation loop yourself\n\n - Experience with running quantitative and qualitative data analyses, interpreting and running statistical tests on evaluation data (natively or with AI tool support), and communicating conclusions to various stakeholders\n\n - A record of scaling and streamlining evaluation quality processes under shipping pressure, with a bias toward documentation and reproducibility over one-off analysis\n\n - Ability to work deeply with domain experts (e.g., ALR / lawyers) and translate nuanced judgment into repeatable, documented standards\n\n - Strong cross-functional coordination across Product, Engineering, Research, ALR, and data providers/vendors\n\n - Clear communicator who can build credibility and tru","salary_min":155400,"salary_max":233200,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["legal","llm","agents","evaluation"],"apply_url":"https://jobs.ashbyhq.com/harvey/f0e4e24a-2a7a-4fc4-8872-9a8a762fecf5/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-04T16:25:51.563Z","expires_at":"2026-11-13T16:55:18.139697Z","created_at":"2026-08-25T18:26:48.998981Z","updated_at":"2026-10-14T16:55:18.142467Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a8bfa422-da52-4371-aecb-c64631f4aaa0"},{"id":"862e6737-af2a-43b2-8d28-3f1aca3b1716","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Machine Learning Manager, Feed Relevance (Retrieval)","slug":"machine-learning-manager-feed-relevance-retrieval-a91727ea","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Reddit is looking for an experienced Engineering Manager to lead our Feed Retrieval team. In this role, you’ll lead a high-impact team of Machine Learning Engineers building the systems that identify, retrieve, and shape the candidate inventory powering Reddit’s personalized feeds. Your team will work at the foundation of Feed Relevance: expanding the set of high-quality content Reddit can recommend, improving personalization and discovery for users across different levels of signal, and building scalable ML systems that directly shape the experiences of over 120M+ daily users. If applying ML / AI in production to improve Reddit Relevance excites you, then you’ve found the right place.\n Responsibilities: \n \n Define Technical Vision \u0026 Strategy: Define the technical vision and long-term roadmap for Feed Retrieval, aligning large-scale recommender-system investments with Reddit’s product, ecosystem, and business objectives.\n Roadmap \u0026 Prioritization: Translate broad Feed Relevance goals into a focused team roadmap, making clear prioritization tradeoffs across model quality, inventory expansion, experimentation velocity, infrastructure cost, and operational reliability.\n Team Leadership \u0026 Development: Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact. \n Technical Execution \u0026 Delivery: Oversee the design, development, and optimization of retrieval systems that source relevant, diverse, fresh, and high-quality candidates for personalized feed experiences.\n Measurement \u0026 Learning: Establish strong measurement, experimentation, and debugging practices so the team can understand retrieval quality, candidate coverage, source incrementality, and downstream impact.\n Platform \u0026 Infrastructure Collaboration: Collaborate with ML platform, infrastructure, ranking, safety, and product teams to build scalable, low-latency retrieval systems that can support the next generation of AI-powered recommendations.\n Operational Excellence: Maintain high standards for system performance, reliability, latency, cost efficiency, and responsible recommendation practices.\n Cross-Functional Partnership: Work with cross-functional partners from across the company to identify key areas of opportunity, set expectations, and communicate your team’s work.\n Recruiting \u0026 Growth: Partner with our incredible recruiting team to attract, interview, and hire diverse and talented machine learning engineers, growing a world-class team.\n \n Qualifications: \n \n Experience Leading ML Teams: 2+ years of experience building and managing high-performing ML or recommender-systems teams.\n Deep ML Expertise: Hands-on experience with large-scale production ML systems, ideally including recommender systems, retrieval models, embedding-based systems, sequence models, transformer-based architectures, or LLM-powered recommendation applications.\n Technical Domain Knowledge: Strong understanding of recommender systems, especially candidate retrieval, embedding/indexing systems, ranking handoffs, feed personalization, exploration, content quality, and measurement strategies. \n Strategic Thinking: Ability to develop and communicate a clear technical strategy across ambiguous problem spaces, balancing user relevance, ecosystem health, system scalability, and business impact.\n Impact-Driven Mindset: Passion for developing scalable, well-designed, and responsible AI solutions that drive business value.\n Exceptional Communication \u0026 Collaboration: Strong interpersonal skills and a collaborative mindset, with the ability to effectively communicate complex technical topics to diverse audiences and build strong relationships with cross-functional partners.\n \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n #LI-remote, #LI-JS5\n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) prog","salary_min":253300,"salary_max":354600,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"junior","tags":["fine-tuning","llm","healthcare","evaluation","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8094985","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T19:24:35Z","expires_at":"2026-11-13T16:57:52.178671Z","created_at":"2026-07-30T14:09:04.880987Z","updated_at":"2026-10-14T16:57:52.181745Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/862e6737-af2a-43b2-8d28-3f1aca3b1716"},{"id":"fe217be4-bb78-4bf9-8a35-0ff491407487","company_id":"b4787255-dacd-444b-8e44-bb9971ec1f36","title":"Principal Product Manager, AI Evaluation and Data Policy","slug":"senior-product-manager-ai-data-foundation-551d6e4e","description":"ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.\n ZoomInfo's Core Data team builds the person and company data that powers our Go-To-Market Intelligence Platform, and we're rebuilding how that data gets made. We're moving from deterministic selection rules to an inference-default model where agents read the evidence and propose answers, and where evaluation — not hand-built logic — decides what ships. We're hiring a Principal Product Manager to own Person Data outcomes end to end and to build the AI evaluation discipline the whole team will run on.\n What You'll Do \n \n Own Person Data end to end, setting the strategy, roadmap, and monthly priorities for contact accuracy, coverage, freshness, and compliance, from cleaning up bogus executive contacts to leadership extraction and person location\n Build and run the AI evaluation engine: define what \"correct\" means per attribute, curate golden sets with the research team, stand up LLM-as-judge gates, and set confidence thresholds for auto-accept, auto-reject, and human adjudication\n Lead the pivot from rules to agents by writing the policy clauses agents follow, proving each step in shadow mode against the gold set, and retiring legacy selection logic once the data supports it\n Decide where AI earns its place in the pipeline and where classical tooling wins, reasoning explicitly about cost per row, latency, determinism, and auditability\n Tackle entity resolution as the hard core of the problem, bringing a clear model for canonical identity, match confidence, and the cost asymmetry between false merges and missed matches\n Own the privacy roadmap for person data, including suppression, opt-out, and notification, in partnership with Legal and Privacy\n Prototype adjudication agents and evaluations in code alongside the AI engineer and data science team, committing directly to the shared repository\n Act as the hub across Person Data engineering, Match, Research, Data Science, Web Data acquisition, and Privacy, and represent the roadmap clearly to product and data leadership\n \n What You Bring \n Must-Have \n \n 8+ years of product management experience, with meaningful time owning data products or data platforms at scale\n Hands-on experience with data pipelines and a working understanding of how records move from ingestion through processing to a served dataset\n Direct experience with entity resolution, record linkage, or matching systems, including canonical IDs, match confidence, survivorship rules, and precision/recall trade-offs\n Demonstrated, current use of AI to improve data systems in production, with the ability to describe how you kept accuracy from degrading\n Experience designing evaluations for model output, including golden sets, precision and recall, LLM-as-judge, human-in-the-loop routing, and production drift monitoring\n \n Preferred \n \n Experience with incremental or streaming data pipelines, rather than batch rebuilds\n Experience with contact, person, or identity data, or with B2B company data\n Familiarity with privacy regulation as it applies to published personal data (GDPR, CCPA, DNC, and international notification regimes)\n Experience with Claude or comparable frontier models, agent frameworks, and eval harnesses\n \n  \n  \n #LI-Hybrid \n #LI-VC1\n Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.\n In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here .\n Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.\n $151,900 — $238,700 USD \n About us:  \n ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.\n ZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.\n ZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available here .\n ZoomInfo is proud to be an equal opportunity employer, hiring ba","salary_min":151900,"salary_max":238700,"location":"Waltham, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["llm","agents","data-pipeline","evaluation"],"apply_url":"https://www.zoominfo.com/careers?gh_jid=8634904002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T15:13:10Z","expires_at":"2026-11-13T17:03:21.837442Z","created_at":"2026-07-16T14:19:52.631092Z","updated_at":"2026-10-14T17:03:21.841216Z","company_name":"ZoomInfo","company_slug":"zoominfo","company_logo_url":"https://www.google.com/s2/favicons?domain=zoominfo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fe217be4-bb78-4bf9-8a35-0ff491407487"},{"id":"f5536ef4-fbd2-4708-bd41-546593698786","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Research Engineer - Evals","slug":"research-engineer-evals-22a46522","description":"RESEARCH ENGINEER - EVALS\n\n\n\nYou'll build the evaluation systems that tell us whether Firecrawl actually works. That sounds simple. It isn't. Our core promise, convert any URL into clean, structured, LLM-ready data reliably, is hard to measure rigorously across millions of different websites, formats, and edge cases. As the systems we're measuring get more complex, the question \"did that work?\" gets harder, not easier.\n\nThis isn't an eval role where you inherit a framework and run benchmarks. You'll design the metrics, build the pipelines, generate the datasets, and own the feedback loop from output quality back to model and product decisions. If you care about what \"good\" actually means and have the engineering depth to measure it, this is the role.\n\n\n\nSalary Range: $250,000–$290,000 USD/year (SF) / $210,000–$224,000 CAD/year (Toronto)\n\nEquity Range: Competitive equity. Details shared during the process.\n\nLocation: San Francisco, CA (SF HQ) or Toronto, ON (Toronto Hub). Hybrid, onsite 3+ days a week.\n\nJob Type: Full-Time\n\nExperience: 4+ years in ML, research engineering, or data-heavy backend, with real evaluation work\n\nWork Authorization: Open to candidates already authorized to work in the US or Canada. In the US, we also support H-1B transfers, F-1 OPT and STEM OPT, TN (Canada and Mexico), and E-3 (Australia). We’re not able to sponsor new H-1B visas for candidates outside the US. For Canada, we’ll consider work permit sponsorship case by case through our Toronto Hub.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data. It's the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nIn September 2026, we raised a $75M Series B led by Smash Capital, and we're spending it building the largest repository of knowledge in the world. We hit 8 figures in ARR in year one and more than doubled it in year two. We have 187k+ GitHub stars, putting us in the top 40 repositories of all time, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves. No hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web. That library is called Alexandria, and it starts now.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design the metrics that define what \"good output\" actually means across millions of sites, formats, and edge cases\n\n - Build the pipelines and harnesses that measure quality rigorously and at scale\n\n - Generate and curate the datasets that make evaluation trustworthy\n\n - Own the feedback loop from output quality back to model and product decisions\n\n - Turn \"did that work?\" into an answer the whole team can act on\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You have the engineering depth to build real evaluation systems, not just run existing ones\n\n - You care deeply about what \"good\" means and how to measure it rigorously\n\n - You're comfortable owning ambiguous problems where the metric itself has to be invented\n\n - You move fast and close the loop. You'd rather ship, measure, and iterate than perfect on paper\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who only wants to run benchmarks someone else designed\n\n - A pure researcher who won't build the systems, or a pure engineer who won't think about methodology\n\n - Someone who needs a fully-specced ticket to start\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: $250,000–$290,000 USD/year (SF) / $210,000–$224,000 CAD/year (Toronto), 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 loo","salary_min":210000,"salary_max":224000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","agents","payments","data-pipeline","search","research","evaluation"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/25092c0e-9a32-4191-af79-050738213704/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-12T23:24:01.87Z","expires_at":"2026-11-13T17:00:13.951476Z","created_at":"2026-05-14T14:16:13.169544Z","updated_at":"2026-10-14T17:00:13.955237Z","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/f5536ef4-fbd2-4708-bd41-546593698786"},{"id":"9a7b6e3f-9ca5-4d74-8149-e52a00eeffdc","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Applied Research - Evals \u0026 Data","slug":"applied-research-evals-data-2b9e0702","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. \n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\nRole Impact\n\nThis is a customer facing role at the intersection of cutting-edge RL/post-training methods, applied data, and agent systems. You’ll have a direct impact on shaping how advanced models are aligned, evaluated, deployed, and used in the real world by:\n\n - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale. Working with applied data from real deployments to continuously refine policies, improve reasoning, and enhance reliability and safety.\n\n - Building Robust Infrastructure: Developing the distributed systems, evaluation pipelines, and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale. Building data capture, processing, and versioning workflows for feedback, model traces, and reward signals.\n\n - Bridge Between Customers \u0026 Research: Translating customer needs and insights from applied data into clear technical requirements that guide product and research priorities. Collaborating closely with RL and eval teams to ensure real-world signals inform model alignment and reward shaping.\n\n - Prototype in the Field: Rapidly designing and deploying agents, evals, and harnesses alongside customers to validate solutions. Using applied evaluation data to iterate on model performance and discover new capabilities.\n\n\nCustomer-Facing Engineering\n\n - Work side-by-side with customers to deeply understand workflows, data sources, and bottlenecks.\n\n - Prototype agents, data pipelines, and eval harnesses tailored to real use cases, then hand off hardened systems to core teams.\n\n - Translate customer insights and evaluation results into roadmap and research direction.\n\n\nPost-training \u0026 Reinforcement Learning\n\n - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks.\n\n - Build evaluation harnesses and verifiers to measure reasoning, robustness, and agentic behavior in real-world workflows.\n\n - Integrate applied data collection and analytics into the post-training process to surface regressions, emergent skills, and alignment opportunities.\n\n - Prototype multi-agent and memory-augmented systems to expand capabilities for customer-facing solutions.\n\n\nAgent Development \u0026 Infrastructure\n\n - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making.\n\n - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements.\n\n - Architect and maintain distributed training and inference pipelines, ensuring scalability and cost efficiency.\n\n - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments.\n\n\nRequirements\n\n - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment.\n\n - Experience with applied data workflows and evaluation frameworks for large models or agents (e.g., SWE-Bench, HELM, EvalFlow, internal eval pipelines).\n\n - Deep expertise in distributed training/inference frameworks (e.g., vLLM, sglang, Ray, Accelerate).\n\n - Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform).\n\n - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL.\n\n - Passion for advancing the","salary_min":150000,"salary_max":300000,"location":"New York, NY","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["llm","reinforcement-learning","distributed-systems","agents","data-pipeline","evaluation","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/bbfe94a6-d1a8-47e9-86af-f117277cdacb/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:34:09.743Z","expires_at":"2026-11-13T16:58:27.495939Z","created_at":"2026-04-13T15:01:32.581029Z","updated_at":"2026-10-14T16:58:27.49992Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9a7b6e3f-9ca5-4d74-8149-e52a00eeffdc"},{"id":"4aa05e90-1b2a-4e5a-b2e0-4035bd5fc1fe","company_id":"b459414f-fd43-42c4-a6e1-f07225286a75","title":"Evaluations - Member of Technical Staff","slug":"evaluations-member-of-technical-staff-8a4ec4b6","description":"ABOUT THE COMPANY\n\nSimile is The Simulation Company. We simulate human behavior to keep people at the center of the decisions that shape the world. With AI, anyone can create a product, a campaign, a policy, or a script — the bottleneck has moved upstream. The hard question is no longer whether you can create something, but what to create, for whom, and how to bring it to life. Those are fundamentally human decisions, and they shouldn't be left to chance or handed off to an algorithm. We're building the infrastructure to understand human behavior at scale and to represent humans in an increasingly agentic world. Our mission is to simulate all eight billion people on earth.\n\n\n\nWe launched five months ago. Since then we've grown revenue 5x, built a new foundation model for human behavior that has run tens of millions of simulations for F100 enterprises, trained a first-of-its-kind confidence model that predicts the accuracy of every simulation, and released the first product that lets organizations verifiably predict the future. The world's leading companies use Simile to make business-critical decisions — from consumer leaders like CVS Health and Wealthfront to professional services organizations like Deloitte and Gallup — strategizing product launches, entering new markets, and forecasting earnings calls.\n\n\n\nWe've raised over $200M at a $2B post-money valuation led by Greenoaks, with Index Ventures, Hanabi, A*, Bain Capital Ventures, and CVS Health Ventures. We've grown from a small home in Palo Alto to a global team of 50+, and we're building a team of the best researchers, engineers, designers, and operators in the world. The future is too important to be left to chance.\n\n\n\n\n\n\n\nABOUT THE ROLE\n\nAs a Member of Technical Staff, Model Evaluations at Simile, you will build the measurement systems that determine whether our simulations of human behavior are accurate, trustworthy, and useful enough to guide real-world decisions. You will help shape what Simile measures, the quality bars we defend, and how evaluation evidence guides model, product, and customer decisions.\n\n\n\nEvaluation at Simile brings together model evals, statistics, behavioral science, research methodology, product quality, and human judgment. Our models simulate people, populations, markets, and groups, which means our evals must reason about distributions, noisy human ground truth, uncertainty, qualitative outputs, behavioral data, and customer decision-making. You will work with unusually rich data about human behavior, including surveys, long-form interviews, customer studies, qualitative research, and behavioral signals such as transactions, product interactions, and other real-world traces.\n\n\n\nWe are hiring across several forms of expertise. Some candidates may be deep in LLM evaluation, model training, and research engineering. Others may bring exceptional strength in statistics, behavioral science, survey methodology, human data, product evaluation, or experimentation. Across backgrounds, we are looking for people who can reason clearly, build quickly, use agentic coding tools fluently, and take hands-on ownership of ambiguous evaluation problems.\n\n\n\nThe core question for this role is simple: How do we know when a simulation of human behavior is good enough to trust?\n\n\n\n\nIN THIS ROLE, YOU WILL:\n\n - Build the measurement layer for behavioral simulation: Design evals, metrics, rubrics, datasets, dashboards, and workflows that measure whether Simile’s models are accurately predicting human behavior across customer use cases, populations, question types, and decision contexts.\n\n - Partner with modeling to improve models: Evaluate new model versions, diagnose regressions, identify priority areas for model-improvement cycles, and maintain stable eval suites that represent capabilities customers actually care about.\n\n - Contribute to product and applied evals: Build evals for qualitative responses, retrieval, survey generation, AI-generated research reports, customer-facing outputs, and other product surfaces where model quality directly shapes customer trust. Turn subjective quality concerns into concrete rubrics, labeled data, automated graders, release criteria, and model-improvement signals.\n\n - Make ground truth and uncertainty legible: Develop rigorous ways to compare simulated responses against human data, customer studies, Simile-collected ground truth, and behavioral datasets. Help the company reason about sampling error, uncertainty, calibration, margin of error, representativeness, and what “ground truth” means when human behavior is inherently noisy.\n\n - Automate evaluation workflows: Use modern agentic coding tools to rapidly build internal tools, inspect model outputs, create labeling workflows, validate evals, and turn fuzzy evaluation questions into working systems. We value people who can compress long, ambiguous projects into fast, useful prototypes without losing sight of rigor or reliability.\n\n - Help define th","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["search","agents","generative-ai","llm","research","evaluation"],"apply_url":"https://jobs.ashbyhq.com/simile/33d75074-c23b-4a1f-bfdb-129bcc5be662/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-30T22:51:51.838Z","expires_at":"2026-11-13T16:58:45.953682Z","created_at":"2026-07-01T14:10:44.230358Z","updated_at":"2026-10-14T16:58:45.956974Z","company_name":"Simile","company_slug":"simile","company_logo_url":"https://www.google.com/s2/favicons?domain=simile.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4aa05e90-1b2a-4e5a-b2e0-4035bd5fc1fe"},{"id":"edd81527-9709-4b01-8b95-c78ef07b4bc1","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer, Simulation Evaluation","slug":"senior-machine-learning-engineer-simulation-evaluation-2b2339ea","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Challenge \n Waymo’s simulator is one of the most complex virtual environments ever built. It blends deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver. The Simulator Evaluation team faces the ultimate data challenge: How do you mathematically prove that a virtual world is \"real\" ?\n We are seeking visionary machine learning engineers and researchers to architect the scalable deep learning systems, novel data workflows, and eval tools that power our research roadmap. In this role, you will pioneer the machine learning and generative vision paradigms required to define and measure the realism of our multimodal world models. Your work will define the state of the art for autonomous simulation, directly steering our research trajectory and the capabilities of the Waymo Driver.\n You will: \n \n Lead the design, development and deployment of cutting-edge evaluation approaches to assess realism of state-of-the-art multimodel world models and generative systems for simulation use cases at Waymo.\n Architect and implement robust and scalable machine learning pipelines for tuning, evaluating, and deploying large-scale discriminator models for the purposes of simulator realism evaluation. \n Evaluate open-source and production-ready video generation techniques that measure realism (e.g. temporal stability, multi-modal consistency, geometric discrepancy, condition following, etc.)\n Apply vision language models to evaluate semantic understanding and controllability across our world simulation products.\n Collaborate with research teams across Waymo and Alphabet to integrate advancements in 4D world modeling and generative AI into production systems.\n Mentor engineers on the team and provide technical guidance on architecture and execution.\n \n You have: \n \n Bachelor's, Master's, or PhD in computer science, machine learning, robotics, or a related field.\n Five or more years of experience in machine learning engineering or applied deep learning, supported by a portfolio of shipped products or peer-reviewed publications.\n Proficient programming skills in Python and hands-on experience with modern machine learning frameworks such as Jax, Flax, or PyTorch.\n Experience designing and implementing evaluation frameworks for complex systems or machine learning models.\n \n We prefer: \n \n Track record of training large-scale generative models (diffusion models, flow matching, vision language models, etc.)\n A PhD and demonstrated success delivering machine learning products focused on 3D generative models, world models, or video generation.\n Experience simulating sensor data, including camera, lidar, and radar, or modeling semantic scenes.\n Experience developing autonomous systems, robotics software, or autonomous vehicle simulations.\n Experience training and optimizing large-scale models on GPU or TPU clusters for efficient production serving.\n Professional experience writing C++ for high-performance production systems.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","autonomous-vehicles","generative-ai","diffusion-models","pytorch","robotics","evaluation","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8001797","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-13T17:49:43Z","expires_at":"2026-11-13T16:55:55.063141Z","created_at":"2026-06-28T14:04:26.926315Z","updated_at":"2026-10-14T16:55:55.066213Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/edd81527-9709-4b01-8b95-c78ef07b4bc1"}],"page":1,"per_page":20,"total":104,"total_is_exact":true,"total_pages":6}