{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will provide technical leadership to a team of researchers and research engineers, mentor and develop highly technical talent, shape research direction, and collaborate with Datadog’s product and engineering teams to translate research advances into products. This role is designed for an exceptional researcher or technical leader who wants to maintain a strong hands-on research identity while developing and leading a high-performing team.  \n At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.  \n What You'll Do:  \n \n Lead research in agent post-training and reinforcement learning, setting the technical direction for ambitious research programs grounded in observability and security\n Guide and mentor researchers and research engineers while remaining closely involved in research strategy, experimentation, model development, and technical problem-solving \n Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments \n Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement\n Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities\n Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks \n \n Who You Are:  \n \n You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling \n You have demonstrated the ability to lead technically ambitious research at meaningful scale, whether in an industry research lab, startup, academic environment, or another research setting \n You have extensive hands-on experience designing or implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks\n You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements \n You have operated at a senior technical level and have experience leading, mentoring, and developing teams of researchers or engineers, while remaining deeply hands-on in technical direction and execution \n You want to develop and support a research team while maintaining a strong technical identity, and you can communicate complex research findings effectively across technical and non-technical audiences \n \n Datadog values people from all walks of life. We know not everyone will meet all the above qualifications on day one. That’s okay. If you’re passionate about technology and want to grow your experience, we encourage you to apply.  \n Benefits and Growth:  \n \n Competitive global benefits \n New hire stock equity (RSUs) and employee stock purchase plan (ESPP)\n Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris \n Opportunity to attend and present at conferences and meetups \n Intra-departmental mentor and buddy program for in-house networking\n An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)  \n \n Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. \n #LI-Hybrid\n Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.\n The reasonably estimated yearly salary for this role at Datadog is:\n $244,000 — $305,000 USD \n \n About Datadog:  \n Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applic","salary_min":244000,"salary_max":305000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","search","agents","healthcare","data-pipeline","research"],"apply_url":"https://careers.datadoghq.com/detail/8279173/?gh_jid=8279173","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:57:23Z","expires_at":"2026-11-13T16:55:38.246957Z","created_at":"2026-10-14T15:19:28.467785Z","updated_at":"2026-10-14T16:55:38.249824Z","company_name":"Datadog","company_slug":"datadog","company_logo_url":"https://www.google.com/s2/favicons?domain=datadoghq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1d5ba28a-37f9-4138-9c77-647324c5d234"},{"id":"2bdabd0b-9451-4388-b502-2ba7d839c43f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, Drive","slug":"senior-machine-learning-engineer-drive-202e134e","description":"About the Team\n DoorDash Drive powers deliveries placed through merchants' own channels—including their websites, mobile apps, and phone orders—using DoorDash's logistics network. The Drive Machine Learning team builds the prediction and intelligence systems that power this business, including delivery and pickup time estimation, merchant prep-time prediction, order release optimization, logistics decision-making, and AI-powered delivery quality signals. Drive presents a unique machine learning challenge. Every merchant has different operational workflows, preparation patterns, and customer expectations, requiring models that generalize across millions of deliveries while adapting to highly diverse merchant behavior. Our team has significant opportunities to improve prediction accuracy, optimize logistics decisions, and build AI-native experiences that directly improve merchant, consumer, and dasher outcomes.\n About the Role\n As a Machine Learning Engineer on the Drive team, you'll own machine learning systems end-to-end—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration. Your work will span several high-impact problem areas:\n \n Build next-generation machine learning models for delivery ETA, pickup ETA, merchant prep-time estimation, and order release prediction that improve reliability for merchants and consumers.\n Develop deep learning models that leverage large-scale spatiotemporal, marketplace, and behavioral signals to improve prediction accuracy.\n Apply reinforcement learning and optimization techniques to improve logistics decision-making, assignment strategies, and marketplace efficiency.\n Build AI-native product experiences using large language models (LLMs) and vision-language models (VLMs). For example, transform pickup photos, item verification flows, receipts, and drop-off images into structured quality signals that help verify orders, prevent delivery defects, and improve issue resolution.\n Design and run rigorous online experiments, production monitoring, and model iteration to continuously improve performance.\n Partner closely with software engineers, product managers, data scientists, and platform teams to bring new machine learning capabilities into production at scale.\n You'll have the opportunity to work across traditional machine learning, deep learning, reinforcement learning, optimization, and multimodal AI while solving some of the most challenging logistics problems at DoorDash.\n \n You're excited about this opportunity because... \n \n You enjoy solving large-scale machine learning problems that directly impact millions of deliveries.\n You have a strong sense of ownership and enjoy taking models from research through production.\n You're comfortable working in ambiguous environments where experimentation and iteration drive product decisions.\n You care about both model quality and production reliability.\n You're excited to work across a diverse set of ML techniques—from neural networks and optimization to multimodal AI.\n You collaborate well across engineering, product, and data science teams.\n \n We’re excited about you because… \n \n 5+ years of industry experience building and shipping production machine learning systems with measurable business impact (Bachelor's, Master's, or PhD).\n Strong experience developing production machine learning models using modern deep learning frameworks such as PyTorch and distributed data processing technologies such as Spark and Airflow.\n Experience building, deploying, monitoring, and maintaining production ML systems end-to-end.\n Strong software engineering skills in Python and experience with modern ML infrastructure and tooling.\n Deep expertise in at least one of the following areas:\n \n Deep Learning\n Reinforcement Learning\n Optimization / Operations Research\n Large Language Models (LLMs) or Vision-Language Models (VLMs)\n \n Experience applying machine learning to estimation, ranking, prediction, optimization, or decision-making problems at production scale.\n Hands-on experience with LLMs or VLMs is a strong plus.\n Experience in logistics, marketplaces, or delivery platforms is helpful but not required.\n Proficiency using AI-assisted development tools (e.g. Claude Code, Codex, Cursor) throughout the software development lifecycle.\n You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.\n Compensation \n The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions.  Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about yo","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","deep-learning","reinforcement-learning","cloud","llm","generative-ai","healthcare","pytorch"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8221807","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:42:26Z","expires_at":"2026-11-13T17:03:28.706911Z","created_at":"2026-10-14T15:25:31.350903Z","updated_at":"2026-10-14T17:03:28.710264Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2bdabd0b-9451-4388-b502-2ba7d839c43f"},{"id":"943a5e5f-5b9a-4c60-ae1f-a9319c87ec0e","company_id":"4bc4e268-7a05-4a65-a162-1688af546f7e","title":"Senior Machine Learning Programmer","slug":"senior-machine-learning-programmer-dd417989","description":"WHAT MAKES US EPIC?\n At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.\n Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.\n PROGRAMMING - SPECIAL PROJECTS\n What We Do \n The Special Projects team at Epic is responsible for executing high-impact projects that push the envelope to define the future of real-time graphics and gaming technology (The Matrix Awakens, Lumen in the Land of Nanite). In collaboration with the Unreal Engine team, we strive to put our technology and knowledge into the hands of users, empowering developers and content creators with the most powerful suite of real-time tools in the world.\n What You'll Do \n You will work with our team of applied research programmers at the intersection of computer vision, gameplay agents, natural language processing, and machine learning to create state-of-the-art tools to support game developers in our ecosystem with a focus on bots (trained gameplay agents, intelligent NPCs, companions, world modeling), as well as automation of a number key areas (including moderation, security, and QA testing). You will be responsible for the design, implementation and deployment of production-ready machine learning models that can be integrated in our ecosystem.\n In this role, you will \n \n Create and deliver production-ready, scalable and high-quality machine learning models and associated algorithms that can make actions in game environments\n Critically assess the effectiveness of such models and make recommendations for the ongoing roadmap\n Methodically understand and assess the effectiveness of new data channels and support collection/creation of new ones\n \n What we're looking for \n \n PhD in Computer Science, Mathematics or related field, or 3+ years of relevant industry experience\n Experience creating / applying machine learning algorithms for action modeling of bots behavior, vision-based or language-instructed, and deploying them as production-level systems in-game\n Expert, hands-on knowledge of:\n \n Deep learning, incl. but not limited to applications in the gaming industry\n Foundational models and vision and/or language, especially vision-language-action models, reinforcement learning agents, incl. local deployment and model post-training of open weights models\n Reinforcement learning, incl. experience with agentic systems, reward design, policy optimisation, evaluation, and building scalable training environments for agents\n Python and associated ML tools/frameworks (numpy, scipy, sklearn, pytorch)\n \n Experience with working collaboratively with other developers, following a flexible/agile/lean approach\n \n This role is open to multiple locations across North America \u0026 Europe (including NYC). \n Pay Transparency Information \n The expected annual base pay range(s) for this position are detailed below. Each base pay range is relevant only for individuals who are residents of or will be expected to work within the specified locale. Compensation varies based on a variety of factors, which include (but aren’t limited to) things such as skills and competencies, qualifications, knowledge, and experience. In addition to base pay, most employees are eligible to participate in Epic’s generous benefit plans and discretionary incentive programs (subject to the terms of those plans or programs). \n New York City Base Pay Range\n $232,666 — $341,243 USD \n ABOUT US\n Epic Games​ ​is a leading interactive entertainment company. For over 30 years we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.\n Like what you hear? Come be a part of something Epic! \n Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment Opportunity (EEO) Policy here .\n Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party (including recruitment or placement agencies) (i.e., a third party with whom we do not have a negotiated and validly executed agreement). We will ","salary_min":232666,"salary_max":341243,"location":"BLANK,BLANK,Multiple Locations","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","pytorch","deep-learning","reinforcement-learning","nlp","computer-vision","machine-learning"],"apply_url":"https://epicgames.com/careers/jobs/6221662004?gh_jid=6221662004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T19:46:26Z","expires_at":"2026-11-13T17:02:44.874623Z","created_at":"2026-10-14T15:24:47.963173Z","updated_at":"2026-10-14T17:02:44.878058Z","company_name":"Epic Games","company_slug":"epic-games","company_logo_url":"https://www.google.com/s2/favicons?domain=epicgames.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/943a5e5f-5b9a-4c60-ae1f-a9319c87ec0e"},{"id":"70e145b8-5b41-4b48-a424-a088b204b2a6","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"[2027] Senior Machine Learning Engineer - PhD Early Career","slug":"2027-senior-machine-learning-engineer-phd-early-career-b6d69cf2","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n At Roblox, we believe advanced research belongs in production, driving core algorithmic advances at a global scale. In this role, you’ll own high-impact projects end-to-end, from initial ideation to production deployment and academic publication, while solving complex, high-dimensional challenges at world-scale concurrency. \n Your expertise, across research areas including Computer Vision and Graphics, multimodality, NLP, or recommendation systems, will directly shape the experience for millions of daily active users. Come build the AI-driven infrastructure powering the future of immersive entertainment.\n You Will \n \n Prototype and implement advanced AI and machine learning solutions that drive core platform features and innovation at scale.\n Develop and deploy cutting-edge models (e.g., deep learning, generative AI, multimodal architectures) to solve complex, high-impact technical challenges.\n Collaborate with applied researchers, engineers, and product teams to translate advanced research concepts into production-ready systems.\n Translate complex research into scalable, robust infrastructure that directly impacts hundreds of millions of daily active users.\n Work backward from user and product needs to architecture, and deliver ML solutions that improve platform engagement, safety, and content ecosystem growth.\n \n You Have \n \n Possess or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.\n Deep expertise and curiosity in a relevant technical domain, such as deep learning, computer vision, NLP, reinforcement learning, or distributed systems.\n Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.\n A strong research track record, evidenced by multiple publications and presentations in rigorous peer-reviewed venues.\n \n You may redact age, date of birth, and dates of attendance/graduation from your resume.\n As you apply, you can find more information about our process by signing up for Speak_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.\n For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page .\n Annual Salary Range\n $196,750 — $243,290 USD \n Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).\n Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.\n For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.","salary_min":196750,"salary_max":243290,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["computer-vision","deep-learning","distributed-systems","generative-ai","nlp","reinforcement-learning","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/8242623?gh_jid=8242623","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T16:05:13Z","expires_at":"2026-11-13T17:02:53.108743Z","created_at":"2026-10-14T15:24:56.108637Z","updated_at":"2026-10-14T17:02:53.113421Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/70e145b8-5b41-4b48-a424-a088b204b2a6"},{"id":"a91d5a21-3259-47d5-8df4-e146e013bc58","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Intern - Labs (Summer 2027)","slug":"software-engineer-intern-labs-summer-2027-48ca1a56","description":"About Labs \n DoorDash Labs is an independent team within DoorDash. We're hiring software engineer interns to work at the intersection of software engineering and robotics to solve key business problems with elegant technical solutions. If you have a passion for applying robotics solutions to a service loved by millions of people, then we want to talk to you! \n About the Teams \n We are hiring Software Engineer interns across our Perception, Autonomy Platform, and DoorDash Air Cloud (Foundry) team. Please review the team descriptions below to learn about each. Selected candidates will be notified of which team is interested in pursuing their candidacy. \n   Perception: The Perception team helps Dot understand the world through vision foundation models and VLMs, supported by lidar and radar for safety. We tackle rare scenarios, adverse weather, and advances in reinforcement learning and end-to-end driving to make deliveries faster and smoother. We're seeking exceptional engineers in machine learning, computer vision, and state estimation to turn cutting-edge research into profitable autonomous delivery. Autonomy Platform: The Autonomy Platform team is responsible for the software foundation that Dot runs on. We build a wide variety of software frameworks for ensuring the robot operates reliably at an ever-increasing scale. This includes everything from designing the robot's operating system, building petabyte-scale data processing pipelines in the cloud, and optimizing nanosecond-level compute of bleeding-edge AI models. We are systems-oriented thinkers and hands-on builders who are excited to advance the state of the art in autonomy. Doordash Air Cloud (Foundry): The Air Cloud team, also known as Foundry, builds the cloud software that powers autonomous delivery across DoorDash—from the moment an order is placed and deemed eligible for drone or robot delivery, through merchant handoff, to flight management.   \n About the Role \n We believe interns are essential to our mission of attracting and retaining top talent. During our 12-week Software Engineer internship, interns are completely immersed in their teams and will have the opportunity to develop, maintain, and ship products. As an intern, you will directly impact our business by collaborating with your team to solve real problems for our customers.\n Applicants must be authorized to work in the U.S. to be considered for this position. F-1 students are eligible if they are using Curricular Practical Training (CPT). DoorDash does not support students on J-1 visas for internships at this time.\n The 2027 internship program will be hosted in person at a Doordash Labs office in either San Francisco or Sunnyvale Labs. You will have an opportunity to select your preferred location(s) and team preferences during the application process.\n You’re excited about this opportunity because you will… \n \n Develop, maintain and ship technical elements with the support of your mentor, manager, and team members\n Act on feedback, coaching, and mentorship from your mentor and team members\n Actively learn about the elements to which you contribute\n Make a direct impact on our business by collaborating with your team to solve problems for our customers\n Participate in intern programming - Leadership Speaker series, social events – virtually and in-person\n Present your summer learnings during a final presentation at the end of the internship\n You are excited about an in person summer work experience \n \n We’re excited about you because… \n \n You are pursuing a B.S. or M.S. Degree in Computer Science or equivalent, have no more than 2 years of full-time work experience, and are graduating between Fall 2027 to Summer 2028.\n You are available for a May or June 2027 start date (Internships are only hosted during the Summer)\n You have experience working with databases (e.g., AWS, SQL, etc.) \n You have a solid understanding of algorithms and data structures\n You have proficient experience working with at least 1 object-oriented programming language (e.g., Python, Java, Kotlin, etc.)\n You have experience writing clean code, working with version control, and doing unit testing. \n \n Preferred but not required  \n \n At least 1 previous Software Engineering internship or equivalent relevant experience\n Curiosity about and experience using AI-powered tools to support software development, problem solving, and learning, with an understanding of how to evaluate outputs and use these tools responsibly\n Ability to analyze and improve efficiency, scalability, and stability of various systems\n Excited to develop, release and run large-scale web applications\n Experience with solutions for systems monitoring, live deployments, and continuous integration\n Experience with real-time technology problems\n Experience working with service-oriented architecture, writing APIs, and designing systems.\n Participation in project or research-based technical work, hackathons, technical conferences/orga","salary_min":107400,"salary_max":158000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["computer-vision","healthcare","fine-tuning","generative-ai","cloud","robotics","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8263774","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T06:50:12Z","expires_at":"2026-11-13T17:03:28.756487Z","created_at":"2026-10-14T15:25:31.397968Z","updated_at":"2026-10-14T17:03:28.760236Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a91d5a21-3259-47d5-8df4-e146e013bc58"},{"id":"774984d7-9daf-4940-96bf-f4a93cc9d946","company_id":"b6e5a3d1-9bde-4a82-8d78-9f38ed99ee81","title":"Staff Research Scientist - Post-Training for Agents","slug":"staff-research-scientist-post-training-for-agents-2ad2fda2","description":"As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in post-training and autonomous agents as a hands-on individual contributor. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products. \n  \n What You'll Do: \n \n Drive research in agent post-training and reinforcement learning, shaping the technical direction of ambitious research programs grounded in observability and security\n Own research problems end to end, from framing the question through experimentation, model development, and evaluation\n Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments\n Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement\n Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor\n Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities\n Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks\n \n  \n Who You Are: \n \n You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling\n You have driven technically ambitious research at meaningful scale as an individual contributor, whether in an industry research lab, startup, academic environment, or another research setting\n You have extensive hands-on experience designing and implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks\n You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements\n You set technical direction through influence rather than authority, and you have mentored other researchers or engineers and elevated the quality of work around you\n You want to stay deeply hands-on in research for the long term, and you can communicate complex research findings effectively across technical and non-technical audiences\n \n  \n Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. \n  \n About Datadog:  \n Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.\n  \n Equal Opportunity at Datadog: \n Datadog is an Affirmative Action and Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.\n  \n Your Privacy: \n Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.\n  \n Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted ","salary_min":320000,"salary_max":400000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","reinforcement-learning","search","data-pipeline","agents","research"],"apply_url":"https://careers.datadoghq.com/detail/8261934/?gh_jid=8261934","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T16:34:22Z","expires_at":"2026-11-13T16:55:38.307935Z","created_at":"2026-10-14T15:19:28.526513Z","updated_at":"2026-10-14T16:55:38.310977Z","company_name":"Datadog","company_slug":"datadog","company_logo_url":"https://www.google.com/s2/favicons?domain=datadoghq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/774984d7-9daf-4940-96bf-f4a93cc9d946"},{"id":"bb310930-8502-4a93-ad2a-1918db8f50ca","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Principal Machine Learning Engineer, Applied AI ","slug":"principal-machine-learning-engineer-applied-ai-2ffeead3","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking a Principal Machine Learning Engineer, Applied AI  with deep expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts.\n Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product.\n This is a high-impact senior IC role for someone who brings deep, hands-on expertise in model post-training, evaluation, and production ML systems, built over many years of solving this exact class of problem.\n What You'll Be Building \n \n Apply deep, hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.\n Lead post-training efforts using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.\n Build and run evaluation loops that measure model quality, reliability, and customer fit, using patterns you've refined across many prior projects.\n Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.\n Partner with AI researchers to translate model advances into reliable, usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug complex model failures using traces, evaluations, customer context, and scientific feedback.\n Mentor engineers and share reusable patterns for model adaptation, evaluation, and deployment, drawing on lessons learned across a long track record of shipping ML systems. \n \n What You'll Need to Succeed \n \n Minimum 2-3 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before.\n Strong software engineering skills in Python and modern ML frameworks like PyTorch.\n A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before.\n Experience leading technical work across research and engineering teams.\n Deep familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. \n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Experience training MoE architectures.\n A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $252,000 — $336,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an en","salary_min":252000,"salary_max":336000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["pytorch","llm","reinforcement-learning","mlops","agents","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4400744009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T15:52:55Z","expires_at":"2026-11-13T17:03:03.861561Z","created_at":"2026-10-14T15:25:07.336193Z","updated_at":"2026-10-14T17:03:03.864854Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bb310930-8502-4a93-ad2a-1918db8f50ca"},{"id":"ec0beeba-9413-474e-88c1-ed020950d565","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior / Staff Machine Learning Engineer, Applied AI","slug":"senior-staff-machine-learning-engineer-applied-ai-80cee647","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking a Senior / Staff Machine Learning Engineer, Applied AI with expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts.\n Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product.\n This is the right role for someone who wants to go deep on model adaptation and evaluation, take real ownership of hard technical problems, and grow into broader scope over time. \n What You'll Be Building \n \n Apply hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.\n Run post-training work using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.\n Run evaluation loops that measure model quality, reliability, and customer fit.\n Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.\n Partner with AI researchers to translate model advances into reliable, usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug complex model failures using traces, evaluations, customer context, and scientific feedback.\n \n What You'll Need to Succeed \n \n Minimum 1-2 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before.\n Strong software engineering skills in Python and modern ML frameworks like PyTorch.\n A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before.\n Deep familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. \n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Experience training MoE architectures.\n A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $180,000 — $298,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.\n We’re All In \n Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran s","salary_min":180000,"salary_max":298000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","mlops","agents","llm","reinforcement-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4400741009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T15:52:17Z","expires_at":"2026-11-13T17:03:04.138761Z","created_at":"2026-10-14T15:25:07.633139Z","updated_at":"2026-10-14T17:03:04.141843Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ec0beeba-9413-474e-88c1-ed020950d565"},{"id":"02955c00-0f56-4cdd-826c-9e251d4a765b","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Director, Machine Learning Engineering, Ads Quality","slug":"director-machine-learning-engineering-ads-quality-2d657f9f","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Pinterest is a visual discovery platform where hundreds of millions of people come to find inspiration and decide what to try, buy, or do next. Our Ads Quality organization builds the machine-learning systems that make ads relevant and valuable to Pinners while delivering meaningful outcomes for advertisers.\n We are seeking a Director of Machine Learning Engineering to lead a broad portfolio of Ads Quality modeling teams focused on engagement, conversion, ROAS optimization, ranking, representation learning, and ML-powered experimentation.\n In this role, you will shape and drive a unified technical strategy across Ads Quality, leading teams responsible for engagement ranking, oCPM and conversion modeling, ROAS optimization, lightweight ranking and retrieval models, foundation model adoption, sequence and multimodal modeling, and the quality and efficiency of production machine learning systems.\n  \n What you’ll do:  \n \n Set the technical vision and multi-year strategy for Ads Quality machine learning, connecting model innovation to Pinner value, advertiser performance, revenue, and marketplace health.\n Lead and develop a group of engineering managers, senior technical leaders, and machine-learning engineers across multiple modeling domains.\n Establish a coherent modeling roadmap across engagement, conversion, ROAS, relevance, ranking, and foundation-model initiatives.\n Drive improvements in model quality, calibration, generalization, cold-start performance, attribution, and robustness across Pinterest surfaces.\n Guide the evolution of Ads models toward larger, more generalizable architectures, including foundation models, distillation, long-context sequence modeling, multimodal representations, and cross-domain learning.\n Ensure that modeling investments translate into reliable production outcomes through strong offline evaluation, online experimentation, launch discipline, and post-launch monitoring.\n Partner closely with Ads Product, Ads Data Science, Ads Signals, Ads Retrieval, Ads Delivery, Measurement, Core, ATG, and ML Infrastructure.\n Set expectations for training-serving parity, data quality, privacy, reliability, latency, capacity, and cost efficiency.\n Improve engineering velocity through better experimentation workflows, reusable modeling infrastructure, automation, and agentic development tools.\n Build a culture of technical excellence, candid collaboration, inclusion, ownership, and continuous learning.\n Represent Ads Quality ML in senior leadership forums and communicate strategy, tradeoffs, risks, and results clearly to technical and non-technical audiences.\n \n  \n What we’re looking for: \n \n Minimum 12 years of experience building and deploying machine-learning systems, including significant experience leading managers and multi-team organizations.\n Demonstrated success leading large-scale recommendation, ranking, advertising, search, marketplace, or personalization ML teams.\n Strong understanding of modern deep-learning and recommender-system techniques, including sequence models, embeddings, multimodal models, multi-task learning, foundation models, distillation, and reinforcement learning.\n Experience with conversion, value, ROAS, bidding, or other lower-funnel optimization problems is strongly preferred.\n Proven ability to connect modeling objectives and offline metrics to online experiments and business outcomes.\n Experience operating production ML systems with demanding requirements for latency, availability, calibration, privacy, reliability, and cost.\n Strong judgment in balancing near-term product delivery with foundational technical investments.\n Track record of building high-performing organizations, developing senior leaders, and creating effective operating mechanisms.\n Excellent communication and collaboration skills, with the ability to inf","salary_min":314580,"salary_max":550515,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","agents","generative-ai","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8258175","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T21:30:23Z","expires_at":"2026-11-13T16:57:51.005164Z","created_at":"2026-10-14T15:21:31.271732Z","updated_at":"2026-10-14T16:57:51.008241Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/02955c00-0f56-4cdd-826c-9e251d4a765b"},{"id":"ad37dc7f-806f-42da-b4c4-c27f68496615","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Sr. Partnerships Manager, US Model Labs","slug":"sr-partnerships-manager-us-model-labs-8cfd3477","description":"About the Role \n As the Sr. Partnerships Manager for US Model Labs, you will own Together AI's relationships with the most influential model builders in the United States: frontier labs, the AI research organizations inside the hyperscalers and major platform companies, chipmakers with their own model programs, and the well-funded independent labs shaping the open-weights landscape. These are the partners whose releases move the market, and your job is to make sure Together AI is the platform they launch on, build with, and route developers to.\n This is a portfolio of a small number of very large, very complex accounts. Unlike a broad ecosystem role, success here is measured in depth rather than breadth: multi-threaded relationships across research, product, legal, and business teams; deals that often bundle model distribution with compute, co-marketing, and joint go-to-market; and the trust required to get early access to models before they ship. You will sit at the intersection of Product, Engineering, Finance, and Marketing, and you will frequently be the person in the room representing Together AI to organizations many times our size.\n You are a strategic deal-maker who is comfortable in long, ambiguous cycles with sophisticated counterparties, and who can turn a single relationship into a durable, expanding partnership.\n Responsibilities \n \n Own the US lab portfolio: Build and execute the partnership strategy for a defined set of major US model labs, including account plans, executive engagement, and a clear view of each lab's roadmap and where Together AI fits in it.\n Land flagship launches: Secure day-zero (or day-one) availability of the most anticipated model releases on the Together platform, coordinating pre-release access, technical bring-up, legal, and launch marketing so we ship alongside the lab.\n Multi-thread across large organizations: Navigate research teams, product leads, developer relations, alliances and partner programs, legal, and executive sponsors to build support for Together AI at every level of the partner org.\n Structure strategic deals: Lead negotiation of distribution, licensing, revenue-share, compute, and co-marketing agreements, including non-standard structures that combine several of these. Handle complex IP, usage-rights, and exclusivity discussions with counterparties that have deep in-house legal and BD teams.\n Product and engineering alignment: Work with Product and Engineering to define bring-up requirements, manage NDA'd early access, and ensure new models are optimized for our inference and fine-tuning stacks ahead of launch.\n Commercial strategy: Partner with Finance to build deal structures that balance aggressive growth with sustainable unit economics, and to model the revenue impact of flagship model availability.\n Go-to-market and co-selling: Collaborate with Partner Marketing on joint announcements and campaigns, and work with Sales to turn lab relationships into enterprise wins through co-selling motions, joint customer engagements, and partner referral programs.\n Be the voice of the labs internally: Bring market and roadmap intelligence back to Product and Leadership, and shape how Together AI positions itself relative to the labs' own platforms and their cloud partners.\n \n Requirements \n \n Enterprise-scale partnership experience: 7–10+ years in Strategic Partnerships, Business Development, or Alliances, with a track record of closing and growing partnerships with large technology companies where the counterparty had its own BD, legal, and partner teams.\n Strategic deal-maker: You have constructed complex, multi-component agreements that don't follow a standard template, and you can hold your own in IP, licensing, revenue-share, and exclusivity negotiations with sophisticated counterparties.\n High EQ and executive presence: You build trust quickly, manage long relationships through changes in personnel and priorities, and are credible in front of senior leaders at partner organizations.\n Technical fluency: While you don't need to be an ML researcher, you should be able to discuss model architectures, context windows, licensing terms, and inference and fine-tuning requirements with both partners and our internal teams.\n AI-native mindset: You work at pace with a relentless sense of urgency, and you prefer \"done and iterating\" over \"perfect and stagnant,\" even while managing accounts that move on their own timelines.\n \n Bonus Points\n \n Direct experience working at, or partnering with, a frontier lab, a hyperscaler AI organization, or a major open-weights model builder.\n Experience with hyperscaler or cloud marketplace partner programs (e.g., alliances, co-sell, or ISV programs).\n Experience in co-selling motions where partnerships directly drove revenue.\n Background at a high-growth API, cloud infrastructure, or developer platform company managing complex ecosystems.\n \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers","salary_min":300000,"salary_max":350000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","reinforcement-learning","pre-training","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5257509007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T19:36:43Z","expires_at":"2026-11-13T16:55:08.950384Z","created_at":"2026-10-14T15:18:58.555014Z","updated_at":"2026-10-14T16:55:08.953226Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ad37dc7f-806f-42da-b4c4-c27f68496615"},{"id":"d355d94d-94d0-4989-a686-a5c733740dd1","company_id":"5fac52d7-9b0b-4990-80a2-e2949dd0af1d","title":"Director of Engineering, Marketing Agent Platform","slug":"director-of-engineering-marketing-agent-platform-35e1e19c","description":"Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.\n  \n Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.\n  \n With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in  Deloitte’s Fast 500  (four years running!),  LinkedIn’s Top Startups ,  Forbes’ Cloud 100 (five years running!),  Inc.’s Best Workplaces , and the  Human Rights Campaign Foundation's Corporate Equality Index !\n About the Role \n Attentive sends billions of messages each year for more than 8,000 brands, helping drive tens of billions of dollars in revenue for our customers. Behind every message is a series of decisions: who should receive it, what it should say, when it should send, which offer to include, and which channel to use.\n We’re building the next generation of that decision-making system: an autonomous marketing agent designed to transform how brands plan, execute, and optimize their marketing. Marketers will set the goals and boundaries, while the platform develops strategies, takes action, measures results, and continuously improves over time.\n In this role, you’ll build the engineering systems that make that vision possible. You’ll work at the intersection of agentic planning, real-time behavioral signals, platform capabilities, experimentation, and reliable execution at massive scale. Your work will help turn increasingly sophisticated AI systems into dependable products that can make and act on millions of marketing decisions.\n The opportunity extends beyond Attentive’s own channels. Through APIs and MCP, we’re building toward a future where this intelligence can operate across a brand’s broader marketing stack, including external email providers, advertising platforms, websites, and custom agents.\n This is an opportunity to help evolve Attentive from a platform that powers messages into one that coordinates and continuously improves a brand’s broader marketing program.\n What You’ll Accomplish \n \n 3+ years managing engineers, 8+ years engineering\n Has seen and helped build engineering excellence at a top-tier organization. Sets an ambitious bar for talent and execution, knows how to hire and lead an A+ team, and brings strong technical opinions with the curiosity to challenge their own assumptions.\n Has worked on building large-scale reinforcement learning systems (not the ML, but the data, infra, and serving platforms beneath it); examples of where you might find these engineers are from frontier labs,  AI-native companies using RL for fine-tuning, AI labeling companies (Handshake, Mercor, ScaleAI), or other tech companies that have built web-scale production-grade reinforcement learning system\n Understands the interface between product engineering, ML, and data science, and is fluent enough to be a real partner. Understands what a model needs from the platform, without needing to own the model.\n Has shipped agentic or LLM-backed systems and knows the production tradeoffs of tool use, orchestration, and retrieval\n Strong on distributed, data-heavy systems: streaming, event-driven, cost-aware inference\n Tracks the field closely, has a real view on what shipped last quarter and why it matters, and has killed their own work when something better arrived\n Still writes code. Hires well, gives direct feedback, holds a high performance bar, and builds accountability without drama.\n \n Your Expertise \n \n AI at massive data scale: You know how to build intelligent systems that take advantage of rich, high-volume behavioral data and translate it into better decisions and experiences.\n Startup mindset, scaled environment: You move quickly, embrace ambiguity, and take ownership while building within the resources and reach of an established Cloud 100 company.\n High-impact ownership: You’re energized by working on one of the company’s biggest strategic bets, with visibility and impact across senior leadership and the board.\n Technical and product leadership: You want meaningful influence over the team, architecture, technical direction, and roadmap, not just a predefined set of engineering tasks.\n \n You'll get competitive  perks and benefits , from health \u0026 wellness to equity, to help you bring","salary_min":340000,"salary_max":390000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","llm","reinforcement-learning","fine-tuning","platform"],"apply_url":"https://job-boards.greenhouse.io/attentive/jobs/4429776009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T15:15:17Z","expires_at":"2026-11-13T17:03:24.644358Z","created_at":"2026-10-14T15:25:28.187597Z","updated_at":"2026-10-14T17:03:24.648539Z","company_name":"Attentive","company_slug":"attentive","company_logo_url":"https://www.google.com/s2/favicons?domain=attentive.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d355d94d-94d0-4989-a686-a5c733740dd1"},{"id":"43b0b20e-4b0d-4936-85cc-fe13c616d09e","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Engineer, Ads Creative Effectiveness","slug":"staff-machine-learning-engineer-ads-creative-effectiveness-0cdfb6f8","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 About the Role \n Reddit’s Ads Creative Effectiveness team sits at the heart of our creative strategy, building GenAI and predictive products that help advertisers ship high-impact campaigns. We are searching for a Staff engineer with in-depth GenAI expertise that can help us build the future of generative products for ad creative at Reddit - best-in-class image, video and copy generators that leverage Reddit’s best practices, performance-guided generations that maximize clicks and conversions, and brand safe, product preserving image/video editing pipelines.\n What you’ll do: \n \n Advanced Generative Workflows: Architect and implement state-of-the-art pipelines for ad creative generation. You will move beyond vanilla prompting to build systems capable of product, font, logo and brand asset preservation using techniques like IP-Adapters, and LoRAs.\n End-to-End Image \u0026 Video Editing: Build editing capabilities allowing advertisers to smart-crop, inpaint, outpaint, resize, and style-transfer assets while maintaining strict brand guidelines.\n LLM/VLM Fine-Tuning: Adapt large vision-language models to understand \"winning\" ad aesthetics. You will build feedback loops where ad performance metrics (CTR, CVR) are used to fine-tune the generation process (RLHF/RLPF for Ads).\n Safety \u0026 Compliance Layer: engineer robust filtering and safety systems. You ensure our models never generate non-compliant, unsafe, or copyright-infringing content.\n Partner Broadly: Collaborate with Product, Sales, Policy, UX, and sibling Ads ML teams to integrate ACE capabilities into the Ad Manager UI and the ad delivery path.\n Guardrails \u0026 Safety: Embed brand-safety filters, copyright checks, and human-in-the-loop escalation paths to protect users and advertisers.\n \n What we’re looking for: \n \n Deep ML Expertise: Hands-on experience with tree models, neural nets, ranking, and especially LLM/VLM fine-tuning \u0026 RLHF/RLPF for ads.\n Quant \u0026 Experimentation Chops: Comfort with uplift metrics, variance, minimum detectable effects and experimentation setups for generative systems.\n Ads / Marketplace Insight: Understanding auction dynamics and advertiser pain points to create tools that drive revenue and retention.\n Startup Mindset: Bias to action, comfort with ambiguity, and scrappiness: ACE evolved from a startup acquisition and still moves fast.\n Communication Superpowers: Able to distill complex ML trade-offs for executives, product managers, and non-technical stakeholders alike.\n Prior work on ad creative tooling: Headline generators, image generators, pre-test models, editing flows for the advertiser.\n \n Nice-to-have \n \n Experience navigating brand-safety, privacy, or copyright frameworks in generative AI products.\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 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) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ .\n To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.\n The base salary range for this position is:\n $230,000 — $322,000 USD \n In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.\n During the interview, we will collect the following categories of personal information: Identifiers,","salary_min":230000,"salary_max":322000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["generative-ai","healthcare","llm","fine-tuning","reinforcement-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8250389","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-02T23:28:54Z","expires_at":"2026-11-13T16:57:52.312642Z","created_at":"2026-10-14T15:21:32.486599Z","updated_at":"2026-10-14T16:57:52.315928Z","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/43b0b20e-4b0d-4936-85cc-fe13c616d09e"},{"id":"530711aa-611a-4a58-bb90-aff9ae5c0837","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff Research Engineer, Multi-Agent Scaling","slug":"staff-research-engineer-multi-agent-scaling-355bd081","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 Large teams of agents are starting to solve problems no single agent can, from rewriting major codebases to formalizing landmark mathematics. Our team studies how these teams scale: what happens to performance, cost and coordination as the number of agents, the compute budget and the length of the task grow, and what has to change to keep getting returns from that scale. We build the platform and evaluations Anthropic uses to run and measure large agent teams, and other research teams build on them.\n This role lives at the boundary between research and engineering. It is a generalist role on a small team: you'll design and run large experiments, build the systems they run on, and get to the bottom of surprising results. We often need to go from a vague question to a running experiment quickly.\n Responsibilities:\n \n Design, run and interpret large-scale experiments on agent teams, reasoning rigorously about what the data does and doesn't show\n Investigate how performance and efficiency change as team size, compute and task horizon grow, and find the bottlenecks that limit them\n Build and scale the systems that run very large agent teams reliably, and debug the failures that only appear at scale\n Design evaluations for long-horizon problems, and keep their results trustworthy\n Build the tooling and metrics that let researchers see what a large agent team is doing and why\n Partner with research teams across Anthropic so they can run their own experiments on the platform, and communicate findings clearly\n \n You may be a good fit if you:\n \n Have significant software engineering, ML or research engineering experience\n Have owned something substantial end to end, such as a large system, an evaluation or benchmark, an agent product, or a research project\n Genuinely enjoy both research and engineering work\n Think quantitatively about complex systems, and think twice before trusting a number\n Can work from a vague question rather than a spec\n Are results-oriented, with a bias towards flexibility and impact\n Have clear written and verbal communication\n Care about the societal impacts of your work\n \n Strong candidates may also have:\n \n Experience building or operating large-scale distributed systems, such as schedulers, sandboxed code execution, or inference and RL infrastructure\n Built evaluations, benchmarks or harnesses for LLMs or agents\n Experience building complex agentic systems that use LLMs\n Experience with scaling laws or other large-scale empirical research\n A background in operations research, statistics, economics, physics, quantitative finance, or another field that models and optimizes complex systems\n \n Strong candidates need not have:\n \n Formal certifications or education credentials\n Academic research experience or publication history\n Prior experience with multi-agent systems or reinforcement learning\n \n Representative projects:\n \n Measure how performance scales with the number of agents on a hard problem, and explain where the curve bends and why\n Prepare our largest-ever agent run: find what breaks as team size and task length grow together, and fix it before launch\n Work out how to allocate a fixed compute budget across a team of agents to solve a problem fastest\n Build tooling that turns the activity of a large agent team into something a researcher can read in minutes\n Design a novel eval that distinguishes real gains in teamwork from artifacts of the evaluation setup\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 $500,000 — $850,000 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 ","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","distributed-systems","search","agents","cloud","llm","alignment","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5442680008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-02T19:26:52Z","expires_at":"2026-11-13T16:54:56.618897Z","created_at":"2026-10-14T15:17:23.088259Z","updated_at":"2026-10-14T16:54:56.622222Z","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/530711aa-611a-4a58-bb90-aff9ae5c0837"},{"id":"e0be16b1-242b-4a96-b22e-736f0755cae8","company_id":"5a1ac926-3bb4-40bb-b952-7dc959ae1820","title":"Tech Lead Manager, Machine Learning, Growth and Marketing   ","slug":"tech-lead-manager-machine-learning-growth-and-marketing-527e3a43","description":"About the Role \n Chime’s Data Science and Machine Learning team is building models, services, and platforms that transform how millions of users manage and grow their financial lives. We are looking for a hands-on Tech Lead Manager with deep technical expertise in machine learning and data science, particularly within the Growth and Marketing domain. Beyond technical proficiency, we value creativity, user empathy, and strong collaboration. \n As a Tech Lead Manager within our Growth and Marketing team, you will lead a talented group of data scientists and machine learning engineers to develop innovative growth and marketing models. These models will provide critical insights into acquiring, retaining and growing Chime members, broaden access to credit, and ensure financial inclusivity. You will play a pivotal role in creating innovative, ground-up products while driving the development of cutting-edge acquisition and retention models and solutions. If you are passionate about marketing, growth, customer acquisition and retention models, this role could be a great fit for you. \n In this role, you can expect to \n \n Lead and inspire a high-performing team of data scientists and ML engineers, ensuring the successful development and deployment of machine learning solutions for customer acquisition, conversion and retention. \n Drive strategic direction for ML initiatives in marketing, engagement, and growth by identifying opportunities where AI/ML can optimize our customer funnel. \n Oversee the end-to-end development of machine learning models such as lifetime value prediction, churn risk modeling, customer segmentation, marketing attribution, referral recommendations, and personalized communications. \n Collaborate cross-functionally with marketing, product, growth, and engineering teams to align machine learning initiatives with business objectives. \n Leverage transactional and behavioral data to enhance customer targeting, optimize acquisition spend, and improve retention strategies. \n Establish ML best practices, including model development, validation, deployment, and monitoring, ensuring scalability and business impact. \n Advocate for a data-driven culture, partnering with business leaders to drive strategic decisions through experimentation and predictive analytics. \n Stay ahead of industry trends, bringing cutting-edge AI/ML techniques into our marketing and growth strategies. \n \n To thrive in this role, you have \n \n 7+ years of experience developing machine learning models for marketing and growth, from inception to production, with a focus on customer acquisition, engagement, and retention. \n 5+ years of experience leading data science teams, with a proven track record of mentoring, coaching, and driving impactful machine learning solutions. \n Strong expertise in marketing and growth analytics, including experience with customer segmentation, LTV modeling, churn prediction, referral systems, and multi-touch attribution. \n Deep understanding of AI/ML techniques, including classification, clustering, reinforcement learning, optimization, deep learning, and NLP for customer engagement. \n Hands-on experience deploying machine learning models in real-world production environments, integrating with marketing tech stacks and growth platforms. \n Strong product intuition with the ability to work iteratively in a fast-paced, cross-functional environment. \n M.S. or Ph.D. in Machine Learning, Computer Science, Statistics, or a related STEM field. \n Proficiency in Python and SQL, with experience in building ML pipelines and wrangling large-scale data. \n Experience with modern ML and data engineering technologies, such as AWS, Kafka, Airflow, Redis, MySQL, Postgres, Spark, Snowflake, Looker. \n Exceptional communication and stakeholder management skills, with the ability to partner effectively with marketing, growth, product, and engineering teams. \n \n #LI-DA1 #LI-Onsite \n Below is the base salary offered for this role and level of experience. Full-time employees may also be eligible for bonus(es), competitive equity, and benefits. For commissioned roles, the base salary listed in this job description does not include incentive/variable pay. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.\n Wage Notice\n $199,000 — $275,000 USD \n A little about us \n At Chime, we believe that everyone can achieve financial progress. We created Chime—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential. \n We're a team of problem solvers, dreamers, and builders with one ","salary_min":199000,"salary_max":275000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["nlp","deep-learning","payments","reinforcement-learning","data-engineering","machine-learning"],"apply_url":"https://boards.greenhouse.io/chime/jobs/8864214002?gh_jid=8864214002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-02T18:01:52Z","expires_at":"2026-11-13T17:03:12.47807Z","created_at":"2026-10-14T15:25:16.005996Z","updated_at":"2026-10-14T17:03:12.481159Z","company_name":"Chime","company_slug":"chime","company_logo_url":"https://www.google.com/s2/favicons?domain=chime.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e0be16b1-242b-4a96-b22e-736f0755cae8"},{"id":"a5d6d300-9651-452a-a441-c56d808d2290","company_id":"a0000000-0000-0000-0000-000000000001","title":"Software Engineer, Sandboxing","slug":"software-engineer-sandboxing-1cf5c2c9","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 Every time Claude runs code, uses a computer, or works as an agent, it does so inside a sandbox. Our Sandboxing team builds the isolation layer behind that work. The same systems run the code Claude writes for users, the environments we train and evaluate models in, and the agents our customers deploy.\n These workloads are untrusted by design, and there are a lot of them. Sandboxes need to start in milliseconds, hold up under adversarial code, and scale to a very large number of concurrent sessions. You will work at the boundary between the Linux kernel, virtualization, and the container ecosystem. You will partner with research, product, and security teams to make sure that safe and fast do not trade off against each other.\n Key responsibilities\n \n \n Design, build, and operate the sandboxing infrastructure that runs untrusted, model-generated code and agent workloads at scale\n \n Improve sandbox startup time, density, and throughput, including snapshot and restore, checkpointing, and warm pools\n \n Strengthen isolation across compute, filesystem, and network, including egress controls and per-session credential handling\n \n Investigate and fix performance, reliability, and security issues at the kernel, hypervisor, and container runtime level\n \n Build the monitoring, tracing, and debugging tools that show where time and resources go inside a sandbox\n \n Work with researchers to support reinforcement learning environments and evaluations that need fast, reproducible, isolated execution\n \n Work with product and platform engineers to expose sandboxes as a dependable, well-designed capability for Claude products and customers\n \n Partner with security teams to threat-model the platform and to test, harden, and respond to escape and abuse scenarios\n \n Work with cloud providers on the hardware and platform features that our workloads depend on\n \n Share knowledge on systems programming and Linux internals, and help set technical standards for the team\n \n Minimum qualifications\n \n \n Software engineering experience in Linux systems programming, kernel development, or another low-level area\n \n Strong programming skills in at least one systems language such as Rust, C, C++, or Go\n \n Working knowledge of virtualization or container isolation technologies such as KVM, QEMU, Firecracker, gVisor, or runc\n \n Understanding of Linux resource management, including namespaces, cgroups, scheduling, and memory management\n \n Experience profiling and debugging performance issues at the system level\n \n Ability to work in unfamiliar codebases and technical areas, and to ship practical solutions with measurable results\n \n Strong communication skills and a commitment to building safe, reliable systems\n \n Preferred qualifications\n \n \n Experience building or operating a multi-tenant sandbox, serverless, or code execution platform\n \n Experience with microVMs, user-space kernels, or lightweight virtualization, including snapshot and restore\n \n Experience with OS-level security hardening, including seccomp, LSMs, and threat modeling of untrusted code\n \n Experience with eBPF and kernel tracing tools\n \n Experience with Kubernetes and cloud infrastructure at scale (AWS, GCP)\n \n Experience with network isolation, high-performance networking, or building egress proxies and policy layers\n \n Experience with sandboxes for AI agents, computer use, or reinforcement learning environments\n \n Experience with GPU virtualization, accelerators, or performance work on machine learning workloads\n \n Prior experience as a technical lead or mentor\n \n Representative projects\n \n \n Cutting sandbox cold start from seconds to milliseconds with snapshot-based restore and pre-warmed pools\n \n Designing a network egress policy layer so agents can reach approved services without exposing secrets\n \n Running many thousands of parallel reinforcement learning environments with strong isolation between them\n \n Building tracing tools that attribute latency and resource use inside a running sandbox\n \n Reducing per-sandbox memory overhead to raise density on each host\n \n Hardening the runtime against escape attempts found in internal red-team exercises\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 $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","alignment","agents","cloud","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5440427008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T15:27:13Z","expires_at":"2026-11-13T16:54:56.554954Z","created_at":"2026-10-14T15:17:23.025457Z","updated_at":"2026-10-14T16:54:56.558118Z","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/a5d6d300-9651-452a-a441-c56d808d2290"},{"id":"ec278267-477d-4688-a758-54877ad70f37","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Machine Learning Engineer II - Learned Planning (Reinforcement Learning)","slug":"machine-learning-engineer-ii-learned-planning-reinforcement-learning-6785c795","description":"About the Company  \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. \n A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. \n Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. \n Meet the Team:   As a Machine Learning Engineer II – Learned Behaviors, you will help develop and deploy  behavior models that power decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will contribute to learned behavior modules that enable safe, efficient, and human-like driving in real-world freight operations.    This role focuses on building, validating, and improving machine learning models and infrastructure that support learned behavior systems within the autonomy stack.    What You’ll Do  \n \n Develop and train machine learning models for learned behavior systems, including approaches such as behavior cloning, imitation learning, and reinforcement learning.\n Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack.\n Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across scenarios.\n Contribute to model training pipelines and data workflows, curating behavior datasets from simulation, fleet logs, and on-vehicle data.\n Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments.\n Help integrate learned behavior models into simulation and testing workflows, enabling faster iteration and more comprehensive validation.\n Support the development of tooling and infrastructure that improves experimentation speed, reproducibility, and model iteration.\n Contribute to technical discussions around model architecture and training strategies within the team. \n \n What You’ll Need to Succeed  \n \n Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience. \n Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments.\n Strong programming skills in Python and PyTorch, with experience writing production-quality ML code.\n Experience training and evaluating machine learning models using large datasets and scalable compute environments.\n Understanding of ML architectures used in autonomy systems, such as transformers, graph neural networks, or sequence models.\n Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines.\n Ability to collaborate with cross-functional teams to integrate ML models into larger software systems. \n \n Bonus Points!  \n \n Experience working in autonomous driving, robotics, or simulation-based training environments.\n Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray).\n Experience working with simulation environments or large-scale behavior datasets.\n Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems.\n Experience deploying ML models into production or real-world robotics systems. \n \n Perks of Being a Full-time Torc’r   Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:     \n \n A competitive compensation package that includes a bonus component and stock options\n 100% paid medical, dental, and vision premiums for full-time employees\n 401K plan with a 6% employer match\n Flexibility in schedule and generous paid vacation (available immediately after start date)\n AD+D and Life Insurance \n \n At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities.  Even if you don’t meet 100% of the qualifications listed for this opportunity, we encourage you to apply. \n For this position, we are open to hiring in Ann Arbor, MI (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United S","salary_min":153200,"salary_max":183800,"location":"Ann Arbor, MI","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["deep-learning","distributed-systems","payments","reinforcement-learning","agents","autonomous-vehicles","pytorch","robotics"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8852888002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T21:15:57Z","expires_at":"2026-11-13T16:56:17.74727Z","created_at":"2026-10-14T15:20:25.93453Z","updated_at":"2026-10-14T16:56:17.749834Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ec278267-477d-4688-a758-54877ad70f37"},{"id":"5e7d165a-b53c-4ed3-9498-73273b6b1e83","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer / Research Scientist, RL Frontiers","slug":"research-engineer-research-scientist-rl-frontiers-f4d3a293","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 Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale.\n This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet.\n Key responsibilities\n \n Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient\n Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale\n Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic\n Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale\n Own end-to-end performance of our largest RL runs, from research code down to the hardware\n Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled\n Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause\n \n Minimum qualifications\n \n Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization\n Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism\n A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact\n Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute\n Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm\n Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack\n \n Preferred qualifications\n \n Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise\n Experience developing RL algorithms for language models\n Experience with scaling laws or other quantitative models of training efficiency\n Experience designing or modifying transformer architectures beyond standard configurations\n Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale\n Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability\n Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices\n Experience with C++ or Rust\n \n Representative projects\n \n Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks\n Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline\n Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch\n Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system\n Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel\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 $500,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["alignment","jax","reinforcement-learning","cloud","pytorch","search","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5438044008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T19:03:37Z","expires_at":"2026-11-13T16:54:56.207892Z","created_at":"2026-10-14T15:17:22.661233Z","updated_at":"2026-10-14T16:54:56.210539Z","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/5e7d165a-b53c-4ed3-9498-73273b6b1e83"},{"id":"f52f56d2-71f3-41b7-97a4-f3faf0cfdfb8","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer / Performance Engineer, RL Distributed Systems","slug":"research-engineer-performance-engineer-rl-distributed-systems-28be6fc4","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 Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is an unusually demanding distributed system. Training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea.\n As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience.\n Our system changes as fast as the research does, correctness under failure matters as much as throughput, and the best solutions often come from understanding the ML workload well enough to know which guarantees it actually needs. Strong candidates have built and run large distributed systems, care about getting the details right, and want to apply that experience to a workload that is very large, very heterogeneous, and changing quickly.\n Key responsibilities\n \n Design, build, and operate the distributed systems that run RL at scale, across training, sampling, and environment execution\n Find and remove whatever currently limits the system, whether it's scheduling, data movement, storage, networking, or coordination\n Build fault tolerance into every layer: failure detection, isolation, and recovery that keep long-running jobs making progress without human intervention\n Design resource management and autoscaling so that compute follows demand as a run's needs shift\n Build observability that makes it possible to understand what a run is doing and why it slowed down, stalled, or produced unexpected results\n Build automation that detects and remediates common problems, and design interfaces that let engineers and automated tools operate runs safely\n Work with researchers and performance engineers to make sure systems changes preserve training correctness and don't introduce subtle nondeterminism\n Remove classes of failure at their source through incident review, testing, and redesign, and write clear design documents for what you build\n \n Minimum qualifications\n \n Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go\n Experience designing, building, and operating large-scale distributed systems in production\n Deep understanding of distributed systems fundamentals, including consistency, coordination, consensus, failure modes, and recovery\n Ability to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and network\n Experience debugging complex failures across many hosts and services, including failures you can't reproduce locally\n Strong written communication, including design documents and incident writeups\n \n Preferred qualifications\n \n Experience running ML training or inference infrastructure at scale\n Experience across several layers of the stack, such as scheduling, storage, networking, and orchestration\n Experience building schedulers, autoscalers, or resource management systems\n Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale\n Experience with high-performance networking, RDMA, or collective communication libraries\n Experience building observability or automated remediation for large fleets\n Experience with async Python frameworks such as Trio or asyncio\n Familiarity with reinforcement learning or large language model training workloads\n \n Representative projects\n \n Design a scheduler that places training, sampling, and environment work across a heterogeneous cluster while respecting network topology and failure domains\n Build a failure detection and recovery system that lets a long-running job survive host and network failures with minimal lost work\n Scale environment execution substantially without increasing tail latency for the training step\n Design an autoscaling policy that rebalances compu","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","llm","search","alignment","distributed-systems","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5438030008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T19:03:34Z","expires_at":"2026-11-13T16:54:56.168786Z","created_at":"2026-10-14T15:17:22.612611Z","updated_at":"2026-10-14T16:54:56.171453Z","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/f52f56d2-71f3-41b7-97a4-f3faf0cfdfb8"},{"id":"a0bd604b-bde2-4b7a-bd1a-1349371e22e1","company_id":"f8de0913-0ef7-4e72-a9cf-81f8513ec624","title":"Research / Software Engineer - Humanoid Whole Body Learning","slug":"research-software-engineer-humanoid-whole-body-learning-efc0c249","description":"FieldAI is transforming how robots interact with the real world. Our growing R\u0026D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.\n\n\nFieldAI is seeking a Software/Research Engineer to help build the learning systems that power our next generation of humanoid robots. You'll work across whole-body loco-manipulation, reinforcement learning, motion retargeting, and real-world robot deployment, turning new research and technical developments into reliable capabilities on physical humanoids. This is a highly hands-on role for someone excited about closing the loop between research, simulation, and robots operating in the real world.\n","salary_min":120000,"salary_max":220000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["search","reinforcement-learning","robotics","research"],"apply_url":"https://jobs.lever.co/field-ai/09def7d8-26bf-451f-a4f2-bd8fd4c5f5cf/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T03:41:03.563Z","expires_at":"2026-11-13T17:00:37.943279Z","created_at":"2026-10-14T15:23:56.946161Z","updated_at":"2026-10-14T17:00:37.946561Z","company_name":"Field AI","company_slug":"field-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=field.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a0bd604b-bde2-4b7a-bd1a-1349371e22e1"},{"id":"0f46b7b7-9959-4d88-96d9-c6227e64bb30","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Research Scientist - Human-AI Systems","slug":"research-scientist-human-ai-systems-eefa08a8","description":"About Snorkel \n Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes.  Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n \n \n We’re looking for a Research Scientist to advance how high-quality data and environments for AI agents are created. You’ll build and optimize pipelines that combine real-world data, automated generation, and human expert input. Working with domain experts, academic partners, customers, and our product and engineering teams, you’ll scale these pipelines to target frontier model performance gaps and expand data and environment diversity. Your work will amplify human knowledge and judgement, enabling experts to create and refine data and agentic environments that strengthens Snorkel’s position as the frontier data lab.\n This role is ideal for someone who wants to advance frontier AI through data and environment creation and enjoys turning research into reusable, scalable systems.\n Location: San Francisco, New York City,  OR REMOTE \n Main Responsibilities \n \n Design, implement, and optimize reusable pipelines that combine AI capabilities with expert judgment to accelerate data and agentic environment creation.\n Design and run rigorous experiments to validate proof-of-concept approaches, measure their impact on data quality, pipeline efficiency, and model performance, and communicate findings.\n Partner with engineering, data operation, domain experts, and customers to turn research prototypes into reliable production workflows.\n Work directly with domain experts to design and test workflows that help them author, review, and refine data and environments.\n Work cross-functionally with data operations, product, and engineering to surface research findings that inform the company roadmap. \n Stay at the frontier of research in data and agentic environment creation and bring best practices into Snorkel's workflows.\n Represent Snorkel's research externally through publications, blog posts, conference talks, and customer engagements that advance the conversation around data-centric AI.\n \n Preferred Qualifications \n \n Strong research background in AI, machine learning, NLP, LLMs, or related fields, with experience developing and evaluating new methods.\n Experience building environments for AI agents in automated research, computer use, coding, or professional domain workflows.\n Experience with one or more of synthetic data generation, human in the loop workflows, reinforcement learning, agent environments, or model evaluation.\n Strong experimental design skills, including defining hypotheses and conducting ablations.\n Experience with software engineering best practices (e.g., clean coding, modular design, version control).\n Ability to collaborate with domain experts and translate their knowledge into concrete tasks, evaluation criteria, and repeatable workflows.\n Comfort with rapid iteration, ambiguous research questions, and moving ideas from experimentation into production.\n Ph.D. in machine learning, NLP, or a related field preferred; equivalent industry or research lab experience considered.\n \n \n  \n Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location.\n Salary range(s) for this role\n $200,000 — $375,000 USD \n Be Your Best at Snorkel \n Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.\n Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, perfor","salary_min":200000,"salary_max":375000,"location":"New York, NY","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","llm","nlp","agents","research"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6208895004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T20:17:42Z","expires_at":"2026-11-13T16:55:33.51421Z","created_at":"2026-10-14T15:19:23.633811Z","updated_at":"2026-10-14T16:55:33.516936Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0f46b7b7-9959-4d88-96d9-c6227e64bb30"}],"page":1,"per_page":20,"total":638,"total_is_exact":true,"total_pages":32}