{"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":"58d65f8c-9bfe-4e0f-8026-f55da835720b","company_id":"26618e2f-35c7-42eb-8f60-bd25a7e9a0d2","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-9c39e036","description":"Ready to do the most impactful work of your career? At  Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for \"good enough,\" you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.”  learn more about working at Coinbase .\n Staff Machine Learning Engineer, CX Intelligence \n  \n CEE Intelligence (CEEI) is the ML and intelligence layer for Coinbase's customer engagement ecosystem, building customer intelligence, evaluation systems, routing and classification models, safety guardrails, and decisioning capabilities that power Help Center, chat, voice, agent tooling, and other customer support runtimes. As a Staff Machine Learning Engineer, you'll lead the design and delivery of production ML systems across the org, shaping technical strategy, building scalable intelligence capabilities, and partnering with product and engineering stakeholders to improve resolution quality, automation coverage, safety, and customer experience. What you'll do: \n \n Own the design and delivery of production ML systems, applying LLMs, deep learning, graph neural networks, and classical ML techniques to solve complex customer experience problems.\n Partner with leadership and cross-functional stakeholders to define strategic roadmaps and translate vision into quarterly execution plans with measurable outcomes.\n Lead high-impact ML projects end-to-end, from architecture through production, managing priorities, risks, and dependencies.\n Build scalable, secure, and reliable ML systems, evaluation pipelines, and data workflows using high-quality, well-tested Python code.\n Mentor engineers and establish team-wide standards for design, testing, observability, and operational excellence.\n Drive design reviews covering security, responsible AI, model quality, and architectural integrity, improving reliability, scalability, and latency across the stack.\n \n Required Skills and Experience: \n \n 8+ years of experience in machine learning and software engineering, with proven success building, scaling, and maintaining production ML systems, data platforms, or analytical services.\n Hands-on expertise with one or more of LLMs, deep learning, graph neural networks, gradient-boosted trees, or regression, combined with strong knowledge of NLP, information retrieval, data mining, or advanced statistics.\n Strong Python skills with demonstrated ability to write maintainable, highly tested production code for ML services.\n Experience mentoring engineers and raising technical standards across a team or organization, including leading design reviews and influencing technical and non-technical stakeholders.\n Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.\n \n Job #: P77568\n Pay Transparency Notice: Base salary varies by location (see range below). Total compensation may also include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)). \n  \n Annual base salary range (excluding equity and bonus):\n $218,025 — $256,500 USD \n \n Application Limit: Candidates may submit a maximum of 3 applications within a 6-month period.\n Equal Opportunity Employer: Coinbase is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or genetic information. Applicants with criminal histories will be considered consistent with applicable federal, state, and local laws. \n US Applicants: View Employee Rights , Know Your Rights , and E-Verify Notice of Participation. \n Accommodations: If you are an individual with a disability who needs a reasonable accommodation, email us your request and contact info at accommodations[at]coinbase.com. Need screen reading technology? Click here to download a free compatible screen reader and view the tutorial . \n Data Privacy \u0026 Arbitration: By submitting your application, you agree to our Candidate Privacy Notice . US applicants: By submitting your application, you agree to Arbitration of Disputes .","salary_min":218025,"salary_max":256500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["generative-ai","llm","nlp","deep-learning","search","machine-learning"],"apply_url":"https://www.coinbase.com/careers/positions/8267151?gh_jid=8267151","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T19:31:10Z","expires_at":"2026-11-13T16:58:01.54146Z","created_at":"2026-10-14T15:21:41.463785Z","updated_at":"2026-10-14T16:58:01.544331Z","company_name":"Coinbase","company_slug":"coinbase","company_logo_url":"https://www.google.com/s2/favicons?domain=www.coinbase.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/58d65f8c-9bfe-4e0f-8026-f55da835720b"},{"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":"bed4ba83-1003-483b-ae16-2e373644bf3e","company_id":"b6e5a3d1-9bde-4a82-8d78-9f38ed99ee81","title":"Staff Research Scientist - Foundation \u0026 World Models ","slug":"staff-research-scientist-foundation-world-models-95a79182","description":"As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in foundation models and world models as a hands-on individual contributor. You will advance large-scale pre-training and multimodal learning across the diverse signals generated by distributed systems, including metrics, traces, logs, topology, and events. 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 foundation models, world models, and multimodal learning, shaping the technical direction of ambitious research programs grounded in observability\n Own research problems end to end, from framing the question through experimentation, model development, and evaluation\n Train large-scale multimodal models on diverse telemetry data, including metrics, logs, traces, topology, events, and other non-text modalities\n Advance approaches to pre-training, representation learning, world modeling, scaling, and evaluation for models that learn the dynamics of complex distributed systems\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 research advances 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 foundation models, world models, multimodal learning, 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, training, and evaluating large-scale deep learning models (such as large language models), with experience in multimodal or non-text data considered a strong plus\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 rei","salary_min":320000,"salary_max":400000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","healthcare","deep-learning","generative-ai","pre-training","search","llm","research"],"apply_url":"https://careers.datadoghq.com/detail/8261937/?gh_jid=8261937","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T16:34:19Z","expires_at":"2026-11-13T16:55:38.303397Z","created_at":"2026-10-14T15:19:28.521017Z","updated_at":"2026-10-14T16:55:38.306288Z","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/bed4ba83-1003-483b-ae16-2e373644bf3e"},{"id":"074ea5ff-c6b6-449e-9c6e-56c3323c5341","company_id":"d8e15a46-b80d-4228-8e7b-34f00357f377","title":"Lead Data Engineer","slug":"lead-data-engineer-5087a7dc","description":"Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.\n What is The Role \n \n  \n We're looking for a Lead Data Engineer to join our Data Engineering \u0026 Architecture team. In this role, you'll help grow a Data Platform that makes it easier for teams across our company to work with data confidently and  effectively . Your work will directly support our business goals not just by keeping data flowing, but by turning it into something people can actually use and trust. \n \n  \n This is a great opportunity for someone who enjoys both the technical craft of data engineering and the human side of it: collaborating with partners, sharing knowledge, and helping others get more out of the tools and systems you build  together . \n  \n \n What You Will Be Doing \n  \n \n You'll design, maintain, and improve the pipelines and infrastructure that power our data platform. Day to day, that means working across the full data lifecycle — from ingestion and streaming to transformation and delivery — using tools like Apache Airflow, Spark, Iceberg, Kafka, Flink, and dbt Labs. \n \n  \n Beyond the technical work, you'll spend meaningful time with stakeholders to understand what they need and help define how data should flow across systems. That includes co-creating data contracts, writing clear documentation, and guiding teammates on how to get the most out of the platform. \n \n  \n A few other things you'll take on: \n \n \n Monitor and refine pipeline performance and refine processes to keep things running smoothly. \n \n Use Terraform to provision and manage resources on Google Cloud Platform (GCP). \n \n Help steward the platform by reviewing code and ensuring good practices and standard processes are followed. \n \n Process access requests and support compliance with platform policies. \n \n What You Bring \n \n \n 8+ years of experience in data engineering or related roles \n \n Experience with Apache Airflow for orchestrating data workflows \n \n Experience with Spark, Flink, Kafka, and dbt Labs for data processing and transformation \n \n Understanding of Apache Iceberg for data table formats and data warehouse and lakehouse strategies \n \n Skilled in using Terraform for infrastructure-as-code deployments \n \n Proficient in programming languages, particularly Bash, Python, and SQL \n \n Knowledge of database systems, including OLAP, OLTP, Document, and Vector databases \n \n Experience with Google Cloud Platform (GCP) for cloud resource management \n \n Bonus: Proven experience with data security and access controls for SOX Compliant data \n \n Bonus: Experience and knowledge of the Elastic stack \n \n Compensation for this role is in the form of base salary.  This role does not have a variable compensation component.   \n The typical starting salary range for new hires in this role is listed below.  In select locations (including Seattle WA, Los Angeles CA, the San Francisco Bay Area CA, and the New York City Metro Area), an alternate range may apply as specified below.  \n These ranges represent the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting.  We may ultimately pay more or less than the posted range, and the ranges may be modified in the future.   \n An employee's position within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs. \n Elastic believes that employees should have the opportunity to share in the value that we create together for our shareholders. Therefore, in addition to cash compensation, this role is currently eligible to participate in Elastic's stock program.  Our total rewards package also includes a company-matched 401k with dollar-for-dollar matching up to 6% of eligible earnings, along with a range of other benefits offered with a holistic emphasis on employee well-being. \n The typical starting salary range for this role is:\n $113,100 — $178,900 USD \n The typical starting salary range for this role in the select locations listed above is:\n $135,900 — $215,100 USD \n Additional Information - We Take Care of Our People \n As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance grea","salary_min":135900,"salary_max":215100,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["embeddings","search","data-pipeline","data-engineering"],"apply_url":"https://jobs.elastic.co/jobs?gh_jid=8247144\u0026gh_jid=8247144","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T18:39:27Z","expires_at":"2026-11-13T16:57:57.540935Z","created_at":"2026-10-14T15:21:37.619706Z","updated_at":"2026-10-14T16:57:57.545432Z","company_name":"Elastic","company_slug":"elastic","company_logo_url":"https://www.google.com/s2/favicons?domain=www.elastic.co\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/074ea5ff-c6b6-449e-9c6e-56c3323c5341"},{"id":"42d0b3d6-fb09-4db9-9c43-b451572a2005","company_id":"da5cfe83-4fb2-4ab3-9392-94069a77ae59","title":"Staff/Senior Staff Software Engineer, Agentic Search","slug":"senior-staff-software-engineer-agentic-search-c4676bc8","description":"Build AI contracting at enterprise scale. Ship it fast.\n\nIronclad is building AI on one of the most consequential datasets in enterprise software: over 2 billion contracts across thousands of the world's largest organizations. Contracts run every dollar, deal, and business relationship on the planet, and the AI transformation of this space is still largely uncharted. Engineers here are building agentic solutions to problems that haven't been solved yet, in a category that's still being defined. The feedback loop is short, the data is rich, and the people closest to the work are the ones deciding what gets built next. That ability to deliver impact quickly is why the the companies at the forefront of AI choose Ironclad to manage their contracts.\n\nWe’re consistently recognized as a leader in the industry: a Leader in the Forrester Wave and Gartner Magic Quadrant for Contract Lifecycle Management, a Fortune Great Place to Work, and one of Fast Company’s Most Innovative Workplaces. Ironclad has also been named to Forbes’ AI 50 and Business Insider’s list of Companies to Bet Your Career On. We’re backed by leading investors including Accel, Y Combinator, Sequoia, BOND, and Franklin Templeton. For more information, visit www.ironcladapp.com http://www.ironcladapp.com or follow us on LinkedIn.\n\n\n\n\nABOUT THE ROLE\n\nIronclad's Intelligence Platform team owns Agent Assistant, Conversational Search, and Content Understanding — the systems that help customers and AI agents understand, find, and act on the right contract information. These are the flagship AI capabilities of our product, built and operated by a combined team of ML and ML infrastructure engineers.\n\nWe have multiple roles open, and are hiring a range of levels — Staff and Senior Staff. As a Staff or Senior Staff Engineer, Agentic Search, you'll own the architecture that combines LLMs and retrieval systems to answer complex, ambiguous questions about a customer's contracts, and you'll set the technical direction that other engineers across the AI organization build on. You'll partner closely with product, applied science, and engineering leaders to raise the company's search quality bar, and you'll bring the technical depth and eval-driven rigor to turn ambiguous problems into shipped, measurable improvements. Scope and ownership will be calibrated to level.\n\n\n\n\nWHAT YOU'LL DO\n\n - Own agentic search architecture. Design and evolve the systems that combine LLMs and retrieval to produce optimal answers to complex or ambiguous questions.\n\n - Drive eval-driven development. Design and run the benchmarks and experiments that measure search quality, and use that feedback to continuously improve the system.\n\n - Raise the search quality bar. Contribute to and influence the company's overall search quality standard.\n\n - Own content understanding and ingestion. Turn raw documents into processed data that retrieval systems can consume, by building and using NLP/LLM models and pipelines.\n\n - Set technical direction. Define architectural decisions and technical direction that other engineers across the AI organization build on.\n\n\nQUALIFICATIONS\n\n - 10+ years building production systems, with a substantial portion in search, information retrieval, content understanding, or recommendation systems at meaningful scale.\n\n - Demonstrated depth in one of: learned/hybrid retrieval (lexical + vector + reranking), query understanding/NLU pipelines, or production LLM agent systems — ideally more than one.\n\n - Experience with search frameworks (Elasticsearch or equivalent — Solr, Vespa, OpenSearch; embedding search) in production, including relevance tuning and reranking.\n\n - Fluency with modern LLM APIs and multi-provider orchestration (Anthropic, OpenAI, Google) — reasoning about token budgets, provider-specific tool-calling semantics, and prompt-caching trade-offs.\n\n - Experience building eval-driven workflows — offline benchmarks, regression detection, structured A/B comparison — as opposed to shipping and hoping.\n\n - Strong autonomy, ownership, and technical leadership across teams, including mentoring senior engineers and driving architectural decisions.\n\n - Comfortable operating in a dynamic, fast-paced, outcome-driven environment.\n\n\nGREAT TO HAVE\n\n - Hands-on experience with post-training algorithms and infrastructure, including SFT and RL.\n\n - Experience with content understanding and/or information retrieval in structured-document-heavy domains.\n\n - Prior work on RAG systems involving data sources in different formats (Google Docs, PDFs, DOCX, etc.).\n\n\n\n\n\nBASE SALARY RANGES\n\n - Staff: $188,000 - $235,000\n\n - Senior Staff: $220,000 - $270,000\n\nThe base salary range represents the minimum and maximum of the salary range for this position based at our San Francisco headquarters. The actual base salary offered for this position will depend on numerous factors, including individual proficiency, anticipated performance, and the location of the selected ca","salary_min":220000,"salary_max":270000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["rag","llm","nlp","agents","search"],"apply_url":"https://jobs.ashbyhq.com/ironcladhq/4be2d35a-9aa0-415c-ba13-30da080158ad/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-05T15:18:25.532Z","expires_at":"2026-11-13T16:58:49.178687Z","created_at":"2026-08-26T13:41:23.863704Z","updated_at":"2026-10-14T16:58:49.181975Z","company_name":"Ironclad","company_slug":"ironclad","company_logo_url":"https://www.google.com/s2/favicons?domain=ironcladapp.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/42d0b3d6-fb09-4db9-9c43-b451572a2005"},{"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":"966ace03-2145-430c-ba1e-b86655a2101e","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Master's University Grad Machine Learning Engineer 2027 (USA)","slug":"masters-university-grad-machine-learning-engineer-2027-usa-bfd4b4f4","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 With more than 640 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers help build personalized experiences to help Pinners create a life they love. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.\n By applying to this role, you will be considered for multiple roles open across our various ML teams. Please only apply once within the USA or Canada as multiple applications may delay our recruitment process. \n This is a general posting for multiple roles open across our various ML teams. You will have the opportunity to work on a very large scope of problems in recommender systems, search, ads, ranking, natural language processing, graph representation learning, personalization, etc. \n As a Machine Learning Engineer at Pinterest, you’ll work on tackling new challenges in machine learning and artificial intelligence. As you kickstart your career at Pinterest, you’ll join our engineering teams as we maneuver through exponential growth and massive scale while building awesome products and features, creating visually rich experiences, spearheading the discovery problem, and pinpointing tomorrow’s engineering challenges. \n What you’ll do: \n \n Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems\n Collect, analyze, and synthesize findings from data and build intelligent data-driven model\n Write clean, efficient, and sustainable code\n Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search\n Design, build, and test models to predict engagement for notifications (push, emails, in-app notifications)\n Build content recommendation systems to power our push, email, and in-app notifications\n Work on state-of-the-art large-scale applied machine learning projects\n Scope and independently solve moderately complex problems\n \n What we’re looking for: \n \n Master's in Computer Science, ML, NLP, Statistics, Information Sciences or related field required\n Machine Learning experience (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc)\n Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data\n Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)\n Proficiency with AI-native engineering, including the design of agent-friendly codebases.\n High degree of autonomy in learning new agent-first development tools.\n Strong critical thinking when working with AI-generated suggestions, with a clear approach to validating correctness, performance, security, and maintainability.\n Comfort iterating on prompts, refining workflows, and adapting AI-assisted approaches based on the problem, context, and constraints.\n Experience in research and in solving analytical problems\n Strong communicator and team player. Being able to find solutions for open-ended problems.\n \n In-Office Requirement Statement: \n \n We let the type of work you do guide the collaboration style. That means we’re not always working in an office, but we continue to gather for key moments of collaboration and connection\n This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of our Pinterest offices. \n \n #LI-HYBRID \n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary","salary_min":146000,"salary_max":196000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["pytorch","tensorflow","computer-vision","search","nlp","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8140219","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T17:00:43Z","expires_at":"2026-11-13T16:57:51.091469Z","created_at":"2026-10-14T15:21:31.340179Z","updated_at":"2026-10-14T16:57:51.094368Z","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/966ace03-2145-430c-ba1e-b86655a2101e"},{"id":"c409314e-e707-469d-980c-8770d7b5d672","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"PhD University Grad Machine Learning Engineer (USA) ","slug":"phd-university-grad-machine-learning-engineer-usa-584220a7","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 With more than 640 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers help build personalized experiences to help Pinners create a life they love. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.\n By applying to this role, you will be considered for multiple roles open across our various ML teams. Please only apply once within the USA or Canada as multiple applications may delay our recruitment process. \n This is a general posting for multiple roles open across our various ML teams. You will have the opportunity to work on a very large scope of problems in recommender systems, search, ads, ranking, natural language processing, graph representation learning, personalization, etc. \n  \n As a Machine Learning Engineer at Pinterest, you’ll work on tackling new challenges in machine learning and artificial intelligence. As you kickstart your career at Pinterest, you’ll join our engineering teams as we maneuver through exponential growth and massive scale while building awesome products and features, creating visually rich experiences, spearheading the discovery problem, and pinpointing tomorrow’s engineering challenges. \n What you’ll do: \n \n Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems\n Collect, analyze, and synthesize findings from data and build intelligent data-driven model\n Write clean, efficient, and sustainable code\n Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search\n Design, build, and test models to predict engagement for notifications (push, emails, in-app notifications)\n Build content recommendation systems to power our push, email, and in-app notifications\n Work on state-of-the-art large-scale applied machine learning projects\n Scope and independently solve moderately complex problems\n \n What we’re looking for: \n \n PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field required\n Machine Learning experience (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc)\n Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data\n Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)\n Proficiency with AI-native engineering, including the design of agent-friendly codebases.\n High degree of autonomy in learning new agent-first development tools.\n Strong critical thinking when working with AI-generated suggestions, with a clear approach to validating correctness, performance, security, and maintainability.\n Comfort iterating on prompts, refining workflows, and adapting AI-assisted approaches based on the problem, context, and constraints.\n Experience in research and in solving analytical problems\n Strong communicator and team player. Being able to find solutions for open-ended problems.\n Preferred Qualifications: \n \n Publications in machine learning, AI, data science, data analytics, statistics, or related technical fields\n Interest in research and in applying ML to impactful real-world problems on the Pinterest product\n \n \n In-Office Requirement Statement: \n \n We let the type of work you do guide the collaboration style. That means we’re not always working in an office, but we continue to gather for key moments of collaboration and connection\n This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of our Pinterest offices.\n \n   \n #LI-HYBRID \n #LI-EB1\n At Pinte","salary_min":174078,"salary_max":229044,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["search","nlp","computer-vision","pytorch","tensorflow","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8140363","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T17:00:35Z","expires_at":"2026-11-13T16:57:51.109891Z","created_at":"2026-10-14T15:21:31.35309Z","updated_at":"2026-10-14T16:57:51.113365Z","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/c409314e-e707-469d-980c-8770d7b5d672"},{"id":"8a8087bb-ca7c-499e-87c8-2fa724bd4274","company_id":"3029e985-56bf-4ac2-9ae1-df4cdd53b12f","title":" Senior Staff Machine Learning Engineer ","slug":"senior-staff-machine-learning-engineer-10188e3f","description":"Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform.\n We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler.\n Role \n We are looking for a Senior Staff Machine Learning Engineer to join our team. This is a remote (USA) role, reporting to the Manager AI Platform and Data Science in the AI Platform and Data Science department. This team’s mission is high-fidelity risk identification in customer data, with goals to catch all threats while minimizing noise. To achieve this, you will develop solutions applying data analysis and threat-research while leveraging AI, machine learning, and data engineering to build quality, data-driven components to automate security analysis.\n What you’ll do (Role Expectations) \n \n Translate risk identification methods into agent logic, understanding the benefits and limitations of agents and ensuring quality across risk analysis, explanations, and recommendations\n Collaborate closely with threat-research to understand data, threats, and their approach to developing security heuristics\n Identify and solve data requirements for analysis, developing and collaborating with data engineering teams for pipelines, enrichments, and aggregations\n Follow a data-driven quality approach to threat detection, including backtesting, balancing precision vs recall, tuning, and quality control\n Deploy and monitor your solutions in production within our CI/CD framework\n \n Who You Are (Success Profile) \n \n You thrive in ambiguity and build the path as you walk it, seeing unstructured challenges as the raw material to construct meaningful systems.\n You act like an owner with a deep passion for the mission, operating with integrity and navigating seamlessly between high-level strategy and hands-on execution.\n You are a continuous learner with a growth mindset who actively seeks feedback to develop yourself, elevate your partners, and execute with purpose.\n You are a positive force who approaches complex technical challenges with contagious, constructive energy and a focus on solutions.\n You are data-driven, using analytics and concrete evidence over assumptions to find the truth, measure what matters, and guide informed decisions.\n \n What We’re Looking for (Minimum Qualifications) \n \n Experience building LLM-powered agents in production: tool and function calling, prompt and context engineering, multi-step orchestration frameworks (LangGraph, LangChain, or equivalent), and evaluation of non-deterministic output against ground truth\n 8+ years of professional Python development with demonstrated ability to design and maintain production-quality systems with validated inputs, data contracts, and comprehensive unit and integration tests, alongside hands-on expertise with SQL and Python data analytics libraries (e.g., pandas, Polars, NumPy)\n Demonstrated ownership of production reliability: CI/CD, containerization, structured logging and metrics, observability and alerting, on-call participation, incident debugging in live distributed systems, and familiarity with cloud and infrastructure-as-code in an AWS environment\n Sr. Staff level ownership and leadership of emergent requirements, architecture, and complex engineering projects\n \n What Will Make You Stand Out (Preferred Qualifications) \n \n Experience with production AI or ML systems where cost, latency, and accuracy are competing constraints, including handling model selection, routing, and regression testing\n Background in cyber threat research, threat modeling, threat hunting, detection engineering, or previous experience in data-driven risk analysis, fraud detection, adversary profiling and targeting, actuarial risk, or close collaboration with risk analysis teams\n Data engineering experience building and operating pipelines over large-volume event data using SQL, columnar or search-backed stores (OpenSearch/Elasticsearch, Athena/Presto), schema evolution, backfills, and data quality validation\n \n #LI-KM9 #LI-Remote\n Zscaler’s salary ranges are benchmarked and are determined by role and level. The range d","salary_min":157500,"salary_max":225000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","distributed-systems","search","cloud","agents","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/zscaler/jobs/5246774007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T00:58:13Z","expires_at":"2026-11-13T16:58:15.601413Z","created_at":"2026-10-14T15:21:55.782436Z","updated_at":"2026-10-14T16:58:15.604658Z","company_name":"Zscaler","company_slug":"zscaler","company_logo_url":"https://www.google.com/s2/favicons?domain=zscaler.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8a8087bb-ca7c-499e-87c8-2fa724bd4274"},{"id":"2902bb5d-3b0a-401a-b725-0f4e066a35d2","company_id":"3029e985-56bf-4ac2-9ae1-df4cdd53b12f","title":" Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-ae4dcbf1","description":"Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform.\n We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler.\n Role \n We are looking for a Senior Machine Learning Engineer to join our team. This is a remote (USA) role, reporting to the Manager AI Platform and Data Science in the AI Platform and Data Science department. This team’s mission is high-fidelity risk identification in customer data. Our goals are to catch all threats while minimizing noise. To do this, you will develop solutions applying data analysis and threat-research, while using AI, machine learning, and data engineering to build quality, data-driven components to automate security analysis.\n What you’ll do (Role Expectations) \n \n Translate risk identification methods into agent logic, understanding the benefits and limitations of agents, and how to ensure quality in different types of tasks, including risk analysis, explanations, and recommendations\n Collaborate closely with threat-research to understand data and threats and their approach to developing security heuristics\n Identify and solve data requirements for analysis, developing and collaborating with data engineering teams for pipelines, enrichments, and aggregations\n Follow a data-driven quality approach to threat-detection: including backtesting, balancing precision vs recall, tuning, and quality control\n Deploy and monitor your solutions in production in our CI/CD framework\n \n Who You Are (Success Profile) \n \n You thrive in ambiguity and are comfortable building the path as you walk it, viewing unstructured dynamic environments as raw material to create meaningful solutions.\n You act like an owner with a deep passion for the mission, navigating seamlessly between high-level strategy and hands-on execution to drive outcomes with integrity.\n You possess a true growth mindset, continuously developing your skills and actively seeking feedback to become a stronger partner and impact-driven teammate.\n You serve as a positive force, approaching complex technical challenges with contagious constructive energy that inspires your team to stay solution-focused.\n You are relentlessly data-driven, leveraging analytics and empirical evidence over assumptions to measure what matters and guide informed decisions.\n \n What We’re Looking for (Minimum Qualifications) \n \n Experience building LLM-powered agents in production: tool and function calling, prompt and context engineering, multi-step orchestration frameworks (LangGraph, LangChain, or equivalent), and evaluation of non-deterministic output against ground truth\n 3+ years of professional Python development, with demonstrated ability to design and maintain production-quality systems with validated inputs, data contracts, and comprehensive unit and integration tests, alongside hands-on expertise with SQL and Python data analytics libraries (e.g., pandas, Polars, NumPy)\n Demonstrated ownership of production reliability: CI/CD, containerization, structured logging and metrics, observability and alerting, on-call participation, and incident debugging in a live distributed system, with familiarity with cloud and infrastructure-as-code in an AWS environment\n Senior-level ownership and leadership of projects, development, and testing\n \n What Will Make You Stand Out (Preferred Qualifications) \n \n Experience with production AI or ML systems where cost, latency, and accuracy are competing constraints, including handling model selection, routing, and regression testing\n Background in cyber threat research, threat modeling, threat hunting, detection engineering, or previous experience in data-driven risk analysis, fraud detection, adversary profiling and targeting, actuarial risk, or close collaboration with these types of teams to understand risk analysis and investigation processes\n Data engineering experience, including building and operating pipelines over large-volume event data: SQL, columnar or search-backed stores (OpenSearch/Elasticsearch, Athena/Presto, or similar), plus schema evolution, backfills, and","salary_min":115500,"salary_max":165000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","search","llm","distributed-systems","cloud","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/zscaler/jobs/5246776007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T00:30:47Z","expires_at":"2026-11-13T16:58:15.596505Z","created_at":"2026-10-14T15:21:55.777646Z","updated_at":"2026-10-14T16:58:15.599725Z","company_name":"Zscaler","company_slug":"zscaler","company_logo_url":"https://www.google.com/s2/favicons?domain=zscaler.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2902bb5d-3b0a-401a-b725-0f4e066a35d2"},{"id":"300cb6b0-c010-49cc-9766-8c76be5ce1ea","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Research Engineer - Benchmarks","slug":"research-engineer-benchmarks-26845c22","description":"RESEARCH ENGINEER - BENCHMARKS\n\n\n\nEvery week, someone asks which data provider is actually best: for company records, for finding the right person, for fresh job listings. Right now the answers come from the vendors themselves. You'll build the independent version. You'll run rigorous, automated, public benchmarks of the providers in Alexandria and beyond, and publish them weekly. The same results will feed straight back into Alexandria so it learns which provider to call for which job.\n\nThis isn't our internal evals role. You're measuring the market, in public, where every number will be challenged by the vendors it ranks. You'll own it end to end: the datasets, the ground truth, the scoring, the harness, the weekly release, and the loop into Alexandria's provider selection. No one hands you a methodology. You write it, defend it, and ship it every week.\n\n\n\nSalary Range: $250,000–$290,000 USD/year (SF) / $210,000–$224,000 CAD/year (Toronto)\n\nEquity Range: Competitive equity. Details shared during the process.\n\nLocation: San Francisco, CA (SF HQ) or Toronto, ON (Toronto Hub). Hybrid, onsite 3+ days a week.\n\nEquity Range: Competitive equity. Details shared during the process.\n\nLocation: San Francisco, CA (SF HQ). On-site, five days a week.\n\nJob Type: Full-Time\n\nExperience: 4+ years in ML, research engineering, or data engineering, with evaluation or benchmark work you've shipped\n\nWork Authorization: Open to candidates already authorized to work in the US or Canada. In the US, we also support H-1B transfers, F-1 OPT and STEM OPT, TN (Canada and Mexico), and E-3 (Australia). We’re not able to sponsor new H-1B visas for candidates outside the US. For Canada, we’ll consider work permit sponsorship case by case through our Toronto Hub.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data. It's the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nIn September 2026, we raised a $75M Series B led by Smash Capital, and we're spending it building the largest repository of knowledge in the world. We hit 8 figures in ARR in year one and more than doubled it in year two. We have 187k+ GitHub stars, putting us in the top 40 repositories of all time, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves. No hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web. That library is called Alexandria, and it starts now.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design and run head-to-head benchmarks of data providers against verified ground truth. For example:\n   \n   - Apollo vs. FullEnrich vs. DataLegion on company details.\n   \n   - FullEnrich vs. DataLegion on finding the right person and their current employer and title.\n   \n   - Built In vs. ZipRecruiter on relevant, fresh, non-duplicate job listings.\n\n - Build and maintain the test datasets and ground truth, and keep them from going stale or leaking.\n\n - Own the automated harness and the weekly benchmark release. Every run has to be reproducible, versioned, and defensible.\n\n - Measure what buyers actually care about: accuracy, coverage, freshness, speed, and cost.\n\n - Work with marketing to ship public leaderboard pages that are useful and hold up under scrutiny.\n\n - Close the loop into Alexandria so benchmark results change which provider gets called for what.\n\n - Keep expanding the categories we benchmark, inside Alexandria and beyond it.\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've shipped evals or benchmarks, and you can explain exactly why your results were trustworthy.\n\n - You've done this somewhere that matters: a frontier lab, a data company like Scale, Surge, micro1 or Mercor, a third-party benchmark org, or a public benchmark project. OSS contributors very welcome.\n\n - You're strong in Python and API integrations, and comfortable with messy vendor APIs, rate limits, and inconsistent schemas.\n\n - You know how to build test sets and scoring methods: sampling, labeling, inter-rater agreement, and when to trust an LLM judge and when not to.\n\n - You use AI heavily and keep upgrading your own workflow. You ship without waiting for instructions.\n\n - You write findings clearly for engineers, marketers, and the vendors on the other side of the leaderboard.\n   \n   \n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who wants to run benchmarks someone else designed.\n\n - Someone who picks the metric that makes the story look good. Our numbers have to survive the vendors","salary_min":210000,"salary_max":224000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","search","agents","llm","payments","research"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/90f88e13-7d5f-43e2-a3fc-9e16d836b71c/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T23:12:06.537Z","expires_at":"2026-11-13T17:00:14.068089Z","created_at":"2026-10-14T15:23:48.951555Z","updated_at":"2026-10-14T17:00:14.074182Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/300cb6b0-c010-49cc-9766-8c76be5ce1ea"},{"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":"eed7d0d7-363e-4ac3-b277-edd857c2eb7c","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior | Staff Software Engineer - AI / ML","slug":"senior-staff-software-engineer-ai-ml-1a499f44","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 In September 2026 we raised a $350 million Series E at a $3.5 billion valuation , and we are scaling our engineering and research teams to meet demand.\n The role\n Frontier AI data is expensive to make and hard to measure. Every task we deliver is tested against the strongest models, often through many long-running agent rollouts. Your job is to make that process faster, cheaper, and more rigorous with ML and AI\n You will be one of the early members of ML \u0026 Research Engineering at Snorkel. You will study how frontier-grade data is generated and evaluated, form hypotheses, validate them against real production data, and ship the winners at scale. You will shape the discipline's direction, its standards, and the team that grows around it.\n What you'll work on\n \n Efficient agentic evals. Cut the cost of long-horizon agent evaluation with adaptive sampling, statistically grounded early stopping, model cascades, caching, and cheap-first gating.\n AI model routing. Route every eval and judge call to the cheapest model that clears the quality bar, with fallback, monitoring, and cost attribution.\n Fine-tuned small models. Fine-tune and serve open-weight models (LoRA and other parameter-efficient methods) where they match frontier quality, and know when they don't.\n Predictive difficulty. Build models that estimate how hard a task is for frontier systems before running a single rollout.\n Measurement for AI data. Build golden datasets, quantify the accuracy and calibration of LLM-as-judge systems, and make quality reproducible across projects.\n Research to production. Turn research prototypes into reusable, configurable components that forward deployed engineers and researchers use on every project.\n \n What you'll bring\n \n 5+ years building production ML or software systems, with end-to-end ownership from prototype to production\n Hands-on experience running LLM or ML workloads in production, and comfort reasoning about non-deterministic systems\n Deep grounding in statistics and experimentation: experiment design, hypothesis testing, sampling, and confidence intervals\n Strong Python and software engineering fundamentals, including testing, code review, and system design\n Experience designing evaluations and interpreting results rigorously\n A habit of finding high-impact problems before they are assigned, and clear communication with researchers, engineers, and business partners\n \n Nice to have\n \n Fine-tuning and serving open-weight models, and judging when a smaller model meets the quality bar\n Building LLM evaluation or experimentation platforms, model gateways, or routing systems\n Experience with agentic workloads, benchmarks, or RL environments\n A record of taking research into production: publications, open-source work, or shipped research-driven features\n MS or PhD in Computer Science, Machine Learning, Statistics, or a related field\n \n Why join now\n \n Frontier problems. Measure and shape the tasks designed to challenge the strongest models in the world.\n All AI, no plumbing. Every problem on this team is an open ML or LLM problem.\n Founding impact. Help define what ML engineering means at Snorkel and grow the team that carries it forward.\n Visible results. Your work shows up directly in the speed, quality, and cost of the data frontier AI is built on.\n Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location.\n Salary range(s) for this role\n $208,000 — $315,000 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 di","salary_min":208000,"salary_max":315000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","fine-tuning","search","llm"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6208931004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T23:25:35Z","expires_at":"2026-11-13T16:55:33.554253Z","created_at":"2026-10-14T15:19:23.670487Z","updated_at":"2026-10-14T16:55:33.557235Z","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/eed7d0d7-363e-4ac3-b277-edd857c2eb7c"},{"id":"e0de87af-ad73-43f6-a502-ac3d19f5bc91","company_id":"4c0fefc3-173a-4227-a823-4d67d3e70ff0","title":"Research Engineer, Climate Modeling ","slug":"research-engineer-climate-modeling-5afb0300","description":"Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.\n Our base salary range is $146,880 - $220,320 and in addition we have generous bonus plans to provide a competitive compensation package.  \n We will begin reviewing applications on October 19, 2026. \n Who You Are: \n We seek a Research Engineer for a new three-year project to develop an AI-powered physics-informed hybrid climate model trained on historical data that is capable of multidecadal forecasts that are more accurate than the current state of the art. We’re looking for someone passionate about improving the quality, efficiency and accessibility of climate models with AI and modern software engineering.  \n This position is supported by a grant with a current period of performance ending September 22, 2029. Continuation of the position beyond that date depends on renewal, extension, or identification of additional funding. Ai2 will make reasonable efforts to notify the employee if funding for the position is expected to end. \n Who We Are:  \n Ai2’s Climate Modeling team is an international pioneer in using machine learning (ML) methodologies to improve on current physics-based climate models.  Our fast, accurate autoregressive open-source emulator, ACE, stably reproduces the climate and weather extremes of an existing climate model or a reanalysis. We have coupled ACE to a ML ocean model, trained it to  realistically account  for changing CO2 concentrations, and downscaled (super-resolved) ACE outputs from their 100 km native scale to to km-scale detail. Our tight-knit team of scientists and software engineers works closely with leading physics-based climate modeling centers to obtain unique training and testing data and get expert feedback on our progress. We collaborate with other leading research groups doing related AI work.\n Your Next Challenge: \n Physically-based climate models discretize equations representing individual time-evolving processes like clouds, rain, wind, land and sea-ice, and ocean currents on a computational grid.  Some processes (e.g. clouds) are less reliably encoded than others (e.g. winds).  Hence such climate models have biases in representing present-day climate and produce an undesirably large range of projections of future climate change for a given human-caused change in CO2 or other climate forcings.  Current AI-based climate models reduce present-day climate bias, but don’t generalize well to future climate change.\n We are starting a 3-year project funded by google.org  to develop an open source ‘AI-first’ climate model that can demonstrably project future climates more accurately than current physically-based climate models when trained on historical observations by judicious design of the AI to incorporate physical principles that reliably apply in any climate, seen or unseen.   The model should be lightweight and easy for an ML-savvy climate scientist to train and deploy. Our starting point is the Google Research NeuralGCM model, a hybrid ML model that uses a conventional discretization of winds encoded in JAX and learns a column-local representation of all other atmospheric and land processes from historical data. Compared to other AI climate models, NeuralGCM uses ML in a conceptually simpler way and shows somewhat better future-climate generalization skill.   It is an open-weight model but its training pipeline  and documentation need to be adapted for a broader user community.\n You will join a small team to develop the desired NeuralGCM+ model and foster its uptake by the climate modeling community.  This includes coupling it to an ML-based ocean model and designing approaches to demonstrably improve its skill in unseen climates.  This position has a three year term; there is potential for longer-term funding if  the NeuralGCM+ project demonstrates exceptional progress.\n You'll get to work on:\n \n Adapting the training pipeline and data needs of NeuralGCM to run efficiently on Google Cloud resources and on NVIDIA GPUs.  Note that NeuralGCM is written in JAX and implemented on TPUs.\n Documenting this training pipeline and distributing code on a GitHub repo.\n Implementing and testing a strategy for coupling NeuralGCM with the chosen ML ocean model.  \n Open-source code development as part of a tightly connected team, including daily meetings, code review, design documents and external collaborators. \n Develop and present tutorials on NeuralGCM+ to interested user groups.\n \n What You’ll Need: \n \n 2-4 years of experience building and deploying a relevant ML application in a practical or academic research setting and be fluent in Python and collaborative coding practices.\n A B.Sc. in computer or computational science, atmospheric science or a related geophysical science, or applied mathematics/statistics.\n Experience i","salary_min":146880,"salary_max":220320,"location":"Seattle, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["search","robotics","jax","research"],"apply_url":"https://job-boards.greenhouse.io/thealleninstitute/jobs/8234447","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T22:53:55Z","expires_at":"2026-11-13T17:02:42.380835Z","created_at":"2026-10-14T15:24:45.312892Z","updated_at":"2026-10-14T17:02:42.383908Z","company_name":"Allen Institute for AI","company_slug":"allen-institute-for-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=allenai.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e0de87af-ad73-43f6-a502-ac3d19f5bc91"},{"id":"e06f09a9-08e9-49e5-a5b6-c6a9742a36c5","company_id":"66e863fb-9aaf-40df-996c-eb439e6f857e","title":"Machine Learning Engineer, Search Quality","slug":"machine-learning-engineer-search-quality-5dc8a594","description":"About Glean: \n  \n Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. \n  \n At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. \n  \n Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. \n  \n If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more. You'll be deeply embedded where people get things done. You'll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. \n  \n About the Role: \n  \n Glean is looking for engineers to help build the world’s best search and assistant product for work. Our engineers work on a range of systems across the stack, including query understanding, document understanding, domain-adapted language models, natural language question-answering, evaluation, and experimentation. We interact regularly with customers, deeply understand their pain points, and use whatever tool is necessary, simple or complex, to solve their problems. \n  \n You will:  \n \n \n Invent new signals to improve the personalization of our search engine \n Train a model to capture interactions between signals in our ranking system \n Design smarter ways to domain-adapt language models to each customer’s corpus \n Discover new ways of combining LLMs with search engines to answer complex questions \n Write robust code that’s easy to read, maintain, and test \n Mentor more junior engineers, or learn from battle-tested ones \n \n About you: \n \n \n 2+ years of experience \n BA/BS in computer science, math, sciences, or a related degree \n Experience working with search, recommendation, natural language processing, or other large systems involving machine learning \n Strong analytical skills and ability to work with data \n Proven ability to design, build, and ship production-ready models \n Proficiency in your ML framework of choice \n Strong coding skills (Python, Go, Java, C++, ...) \n Thrive in a customer-focused, tight-knit and cross-functional environment - being a team player and willing to take on whatever is most impactful for the company is a must \n A proactive and positive attitude to lead, learn, troubleshoot and take ownership of both small tasks and large features \n \n Location:   \n \n \n This role is hybrid (4 days a week in our San Francisco office) \n \n Compensation \u0026 Benefits: \n  \n The standard base salary range for this position is $140,000 - $265,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. \n  \n We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused. \n  \n We’re committed to building and sustaining a diverse, inclusive workplace. We strive to attract and retain people with a wide range of backgrounds, experiences, and perspectives, and we do not discriminate on the basis","salary_min":140000,"salary_max":265000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["agents","search","nlp","cloud","llm","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/gleanwork/jobs/4738120005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T18:50:37Z","expires_at":"2026-11-13T16:55:35.144081Z","created_at":"2026-10-14T15:19:25.279367Z","updated_at":"2026-10-14T16:55:35.147075Z","company_name":"Glean","company_slug":"glean","company_logo_url":"https://www.google.com/s2/favicons?domain=glean.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e06f09a9-08e9-49e5-a5b6-c6a9742a36c5"},{"id":"27565f6d-c820-4c48-95a1-521de6cdd7f9","company_id":"2721f049-2cf2-4e3e-82d0-8d8df89c8f90","title":"Senior Manager, SEO \u0026 GEO","slug":"senior-manager-seo-geo-3c5edc92","description":"About Nebius: \n Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.\n Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\n Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R\u0026D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R\u0026D.\n The role \n Nebius is looking for a seasoned digital marketer to own our global SEO and Generative Engine Optimization (GEO) strategy end to end. This is not a coordination-only role: you will set the strategy, build the measurement framework, identify the highest-value opportunities, translate insight into clear recommendations, and drive implementation with content, web, product marketing, engineering, and analytics partners.   \n You will be the company’s subject-matter expert for how Nebius is discovered, understood, cited, and selected across traditional search engines and AI-powered answer experiences. The right person has already built and scaled an organic growth program, can point to measurable commercial outcomes, and is comfortable operating independently from day one in a fast-moving global environment.   \n You’re welcome to work remotely in the United States. \n Your responsibilities will include:   \n \n Set the GEO/AIO strategy and drive execution.   Define priority topics and target LLMs, and develop tailored content, citation and authority-building tactics to grow Nebius’s visibility, prominence and share of voice in AI-generated answers.   \n \n \n Set the SEO strategy and drive execution.   Define priority keywords and search terms, and lead technical SEO, on page optimization to achieve and sustain top organic search rankings.   \n \n \n Turn analytics into action . Create a unified measurement framework and dashboards covering organic visibility, non-branded demand, rankings, traffic quality, engagement, conversions, pipeline influence, share of voice, AI citations, referral traffic, and content performance.   \n \n \n Find and scale winning themes . Use search, audience, competitive, conversion, and AI visibility data to identify topics where Nebius can win. Translate those insights into prioritized content recommendations, briefs, refresh plans, and scalable programs for content owners.   \n \n \n Optimize content directly when needed. Audit and improve existing pages for intent, information architecture, clarity, internal linking, metadata, structured data, conversion paths, and retrievability by search engines and large language models.   \n \n \n Lead technical web optimization . Partner with web and engineering teams on crawlability, indexation, rendering, performance, internationalization, schema, migrations, site architecture, redirects, canonicalization, and other technical foundations.   \n \n \n Build GEO and AEO capability . Define how Nebius measures and improves presence across AI assistants and answer engines. Test the content formats, source signals, structured information, digital PR, and authority patterns most likely to earn accurate citations and recommendations.   \n \n \n Run an experimentation program . Design and evaluate tests across templates, page structures, content formats, calls to action, internal links, and conversion journeys; turn results into repeatable playbooks.   \n \n \n Build an AI-native operating model . Use and, where valuable, build AI agents to automate repeatable research, monitoring, analysis, content briefing, quality assurance, and optimization tasks, improving speed and scale without lowering editorial or analytical standards.   \n \n \n Create operating discipline . Establish prioritization, governance, documentation, editorial standards, reporting cadences, and quality checks that make organic growth a durable cross-functional capability.   \n \n \n Advise senior stakeholders . Communicate opportunities, trade-offs, progress, and business impact clearly to marketing and company leadership; challenge assumptions with evidence and make decisive recommendations.   \n \n What success looks like: You are a system thinker with equal parts strategist, analyst, operator, and editor. You have enough technical depth to diagnose foundational issues, enough content judgment to improve how a complex story is told, and enough commercial instinct to prioritize the work that will matter most. You are comfortable being the expert in the room, but you build trust through evidence, clarity, and collaboration. Most importantly, you have done this before, and you are ready to own it again at global scale.   \n \n A clear baselin","salary_min":150000,"salary_max":187500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["agents","llm","search","computer-graphics","cloud"],"apply_url":"https://careers.nebius.com/?gh_jid=4986889101","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T16:14:26Z","expires_at":"2026-11-13T17:00:03.492371Z","created_at":"2026-10-14T15:23:35.220302Z","updated_at":"2026-10-14T17:00:03.495446Z","company_name":"Nebius","company_slug":"nebius","company_logo_url":"https://www.google.com/s2/favicons?domain=nebius.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/27565f6d-c820-4c48-95a1-521de6cdd7f9"},{"id":"62db2168-bd15-49b2-b85d-c16fb830a1b2","company_id":"4bc4e268-7a05-4a65-a162-1688af546f7e","title":"Principal Research Engineer, Unreal Assistant","slug":"principal-research-engineer-unreal-assistant-b6414563","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 - UNREAL ENGINE\n What We Do \n Unreal-powered projects have been on the bleeding edge of real-time entertainment for over 20 years. Our team of programming experts are always innovating to improve the tools and technology that empower content developers worldwide.\n What You'll Do \n We're looking for a Principal Al Programmer who builds products that integrate Al into Unreal Engine. You'll design and ship the full stack of systems that let LLMs act inside the engine (from end to end, not just a single layer), bringing depth in game engine architecture, cloud services, and how LLMs actually work.\n In this role, you will \n \n Design and build LLM-driven agents and tools that operate directly inside Unreal Engine, including engine-side C++/Python tooling and editor scripting\n Design and extend the cloud-hosted agent harness, services, and database that mediate between LLMs and the engine\n Work across the full stack (engine tooling, cloud backend, and the web UI used to drive and monitor agents) to ship end-to-end features\n Evaluate and integrate emerging LLM capabilities (tool use, multi-agent orchestration, context management) into Epic's engine and pipeline tooling\n Define technical direction for the reliability, safety, and evaluation of LLM-driven engine automation\n Partner closely with engine, tools, and backend programmers, and mentor others on both game engine internals and LLM system design\n \n What we're looking for \n \n Strong software programming background with hands-on experience in a game engine (Unreal Engine strongly preferred), including engine architecture and tooling/editor scripting\n Practical experience building or shipping products powered by LLMs (agents, tool-calling, RAG, prompt/context programming), or a strong conceptual understanding of how LLMs work\n Experience with cloud-native services and infrastructure: web APIs, databases, and service architecture and deployment\n Excellent programming skills across C++ (engine-side) and Python and/or a web backend language\n Ability to work full-stack and reason about tradeoffs across engine code, backend services, and a web front end\n Excellent written and verbal communication skills\n BS/MS in Computer Science or a related field\n \n This role is open to multiple locations in North America (including CA, NYC, \u0026 WA). \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 $245,975 — $360,763 USD \n California Base Pay Range\n $270,572 — $396,838 USD \n Washington Base Pay Range\n $245,975 — $360,763 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 not pay any fees to any unauthorized third party. Further details on these matters can be found here .","salary_min":245975,"salary_max":360763,"location":"BLANK,BLANK,Multiple Locations","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["llm","agents","search","research"],"apply_url":"https://epicgames.com/careers/jobs/6200782004?gh_jid=6200782004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T13:51:18Z","expires_at":"2026-11-13T17:02:44.861895Z","created_at":"2026-10-14T15:24:47.951049Z","updated_at":"2026-10-14T17:02:44.864932Z","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/62db2168-bd15-49b2-b85d-c16fb830a1b2"}],"page":1,"per_page":20,"total":682,"total_is_exact":true,"total_pages":35}