{"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL","slug":"research-engineer-performance-rl-2f0da25a","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 RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\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 $350,000 — $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 you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["pytorch","gpu","code-generation","search","llm","jax","reinforcement-learning","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-05-19T14:00:27.756108Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-04-19T14:00:27.837188Z","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/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"60c7aa2a-21b2-4ed4-997e-01e06f7425d0","company_id":"a0000000-0000-0000-0000-000000000003","title":"Director, Enterprise Machine Learning \u0026 Research","slug":"director-enterprise-machine-learning-research-1923b033","description":"The Enterprise ML team works on the front lines of the AI revolution, partnering deeply with customers to identify high-impact business problems and build cutting-edge AI systems using Scale’s proprietary research, data, and infrastructure—unlocking domain expertise through high-quality data and expert feedback.\n As Director of Enterprise ML, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.\n This role is ideal for a leader who thrives in ambiguity, understands both frontier GenAI capabilities and their limitations, and is motivated by turning research into durable, production-ready systems.\n What You’ll Do \n \n Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).  \n Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.\n Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.\n Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.\n Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.\n Stay deeply connected to the research community, understanding major trends, and helping set them.\n Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.\n \n What We’re Looking For \n Core Qualifications \n \n 8+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.\n A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).\n Experience and track of recording in landing major research impacts in a fast-paced environment\n Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.\n Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.\n Exceptional communication and stakeholder management skills, with the ability to influence executives, customers, and cross-functional partners\n \n Nice to Have \n \n Hands-on experience building and deploying agent-based, tool-augmented, or workflow-driven LLM systems in enterprise environments\n Prior ownership of enterprise AI platforms, internal ML products, or customer-facing AI services at scale\n Proven track record of partnering directly with enterprises to identify high-impact use cases and deliver measurable business outcomes\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:\n $289,800 — $362,250 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government","salary_min":289800,"salary_max":362250,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["llm","generative-ai","deep-learning","research","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4679727005","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-31T18:05:38Z","expires_at":"2026-05-19T14:01:12.634984Z","created_at":"2026-04-13T09:36:42.207592Z","updated_at":"2026-04-19T14:01:12.707568Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/60c7aa2a-21b2-4ed4-997e-01e06f7425d0"},{"id":"f8c6c621-b459-40f6-b41d-0baa191734ff","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Lead, Training Insights","slug":"research-lead-training-insights-6091f430","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 As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterize model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.\n Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organizational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.\n This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.  \n Responsibilities:  \n \n Build new novel and long-horizon evaluations\n Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training\n Lead strategic evaluation coverage across the company\n Shape the evaluation narrative for model releases\n Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research\n Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules\n Build and maintain relationships across Anthropic's research organization to ensure evaluation insights inform training and deployment decisions\n Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices\n \n You may be a good fit if you:  \n \n Have significant experience designing and running evaluations for large language models or similar complex ML systems\n Have led technical projects or teams, either formally or through sustained ownership of critical research directions\n Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly\n Think strategically about what to measure and why, not just how to measure it\n Can synthesize information across multiple teams and workstreams to form a coherent picture of model capabilities\n Communicate complex technical findings clearly to both technical and non-technical audiences\n Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings\n Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed\n \n Strong candidates may also have:  \n \n Experience building evaluations for long-horizon or agentic tasks\n Deep familiarity with Reinforcement Learning training dynamics and how model behavior changes during training\n Published research in machine learning evaluation, benchmarking, or related areas\n Experience with safety evaluation frameworks and red teaming methodologies\n Background in psychometrics, experimental psychology, or other measurement-focused disciplines\n A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment\n Experience managing or mentoring researchers and engineers\n \n Representative projects:  \n \n Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions\n Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge\n Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritizing new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product\n Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterize model capabilities and limitations\n Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks\n Leading a team","salary_min":850000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["pre-training","agents","alignment","search","reinforcement-learning","llm","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5139654008","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-06T17:15:29Z","expires_at":"2026-05-19T14:00:29.389271Z","created_at":"2026-04-13T09:36:01.625992Z","updated_at":"2026-04-19T14:00:29.467189Z","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/f8c6c621-b459-40f6-b41d-0baa191734ff"},{"id":"7c9f83a4-20ba-4a40-bf23-8ca2952b1970","company_id":"34cd55a7-59d0-4c54-bde4-216aadd50eae","title":"Forward Deployed Engineer, Strategic Accounts","slug":"forward-deployed-engineer-strategic-accounts-20731622","description":"About HeyGen \n At HeyGen, our mission is to make visual storytelling accessible to all. Over the last decade, visual content has become the preferred method of information creation, consumption, and retention. But the ability to create such content, in particular videos, continues to be costly and challenging to scale. Our ambition is to build technology that equips more people with the power to reach, captivate, and inspire audiences. Learn more at  www.heygen.com .  Visit our Mission and Culture doc here . \n About the role\n HeyGen is building the AI video platform that Fortune 500 enterprises run their content operations on. Our strategic customers are not looking for another SaaS tool. They want production AI systems that plug into their LMS, their CRM, their HR stack, and their brand controls, and that their employees and end-users actually use every day. Closing the gap between what our APIs can do and what each enterprise needs them to do is the job.\n We're hiring our first Forward Deployed Engineers to own that gap.\n As a Forward Deployed Engineer on Strategic Accounts, you'll embed with our most technically complex enterprise customers and ship production AI video systems alongside their engineering teams. You'll own the last mile of delivery: discovery, technical scoping, architecture, build, rollout, and the eval-driven feedback loop back into HeyGen's product and model roadmap.\n You'll operate autonomously, partner directly with customer engineering leaders and VPs, and represent HeyGen at the technical frontier. Typical questions you'll help answer look like:\n \n \"How do we let 40,000 customer service reps generate branded training videos in twelve languages without going through our marketing team?\" \n \"How do we embed a live avatar into our product onboarding flow so every new user gets a conversational walkthrough in under 20 milliseconds of latency?\" \n \"How do we make HeyGen safe to deploy across a regulated pharma's field medical team with full avatar consent, audit logging, and subprocessor controls?\" \n \n You'll work side by side with our Product, Engineering, Sales, and Customer Success teams. Your field insights will directly influence what HeyGen ships next. San Francisco or Los Angeles. Hybrid. Expect 25–40% travel to customer sites. \n What you'll do \n \n Own end-to-end enterprise deployments. Lead technical discovery, architecture, and production rollout for our most complex strategic accounts. Integrate HeyGen cleanly into customer CRM, LMS, HR, and marketing stacks.\n Build on the HeyGen API and LiveAvatar. Ship real-time conversational agents, session-based lifecycles, and WebRTC integrations inside client applications. Produce reusable reference implementations, SDK wrappers, webhook integrations, and eval harnesses that later get pulled back into the platform.\n Run the hard escalations. Act as the senior technical owner on production-blocking issues. Advocate for the customers’ technical needs on streaming, latency, and integration failures, and coordinate cross-functional fixes with engineering.\n Close the product feedback loop. Translate messy real-world requirements into structured product input. Identify patterns across custom builds that should be generalized into the platform, and partner with core engineering to productize them.\n Be a trusted technical voice for customers. Explain model behavior, performance trade-offs, and roadmap to non-technical executives and to customer engineering teams in equal measure.\n \n What success looks like \n In your first year, we'll measure your impact on time to production for strategic accounts, expansion revenue driven by the accounts you own, the number of reusable patterns you contribute back to the platform, and the quality of the eval and feedback signal you send into our product organization.\n What we're looking for \n \n 3+ years in software engineering, solutions architecture, or a similar builder role. You're comfortable shipping production-grade code in Python and JavaScript/Node.js.\n Hands-on experience building with LLMs or other generative AI systems in production, including familiarity with prompt engineering, agent development, and evaluation frameworks. You understand how model behavior affects product experience.\n Working knowledge of API design, webhooks, and cloud infrastructure.\n The ability to move fluidly between a customer engineering standup and a VP-level business conversation, and to explain technical trade-offs to both audiences without losing either.\n High agency under ambiguity. You're energized by building a function that doesn't exist yet rather than joining one that does.\n \n Nice to have \n \n WebRTC, real-time media, or low-latency streaming experience.\n Experience at an FDE, solutions engineering, or post-sales engineering org at a frontier AI or enterprise SaaS company.\n Experience as a technical founder or early engineer at a startup. We especially want to hear from you.\n \n A note on fit \n If you don't ch","salary_min":175000,"salary_max":225000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["api-design","generative-ai","llm","cloud"],"apply_url":"https://job-boards.greenhouse.io/heygen/jobs/5113581007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-18T00:32:43Z","expires_at":"2026-05-19T14:17:10.391548Z","created_at":"2026-04-19T14:17:10.468259Z","updated_at":"2026-04-19T14:17:10.468259Z","company_name":"HeyGen","company_slug":"heygen","company_logo_url":"https://www.google.com/s2/favicons?domain=heygen.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7c9f83a4-20ba-4a40-bf23-8ca2952b1970"},{"id":"0a14bfc3-7578-44dc-b18d-c0b4bc052b6d","company_id":"4bc4e268-7a05-4a65-a162-1688af546f7e","title":"Machine Learning Engineer, Discovery Recommendations","slug":"machine-learning-engineer-discovery-recommendations-e850967c","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 ANALYTICS\n What We Do \n Our Data \u0026 Analytics teams build powerful stories and visuals that inform the games we make, the technology we develop, and business decisions that drive Epic.\n What You'll Do \n You will design, build, and optimize the recommendation systems that power Fortnite's Discover experience, serving personalized recommendations to one of the largest player bases in gaming across a massive catalog of creator-built experiences.\n You'll work across the full recommendation stack: candidate generation, content ranking, impression allocation, and real-time reranking.\n Unlike recommendation systems that operate over a stable catalog, you're working with a massive, rapidly changing content library where new experiences are published daily, quality signals are sparse, and the system's own outputs shape the data it learns from.\n In this role, you will \n \n Design and implement retrieval, ranking, and reranking models for creator content using deep learning approaches (two-tower architectures, transformer-based sequence models, embedding-based retrieval) and build the user representation systems that power personalized discovery\n Build and optimize multi-stage candidate generation and impression allocation pipelines that balance relevance, diversity, and fair content exposure across a large and rapidly evolving catalog\n Design and run A/B experiments to validate model improvements, own evaluation frameworks that capture recommendation quality holistically, and drive the path from experiment to production deployment\n Collaborate with analytics and content quality teams on ranking signals including genre classification, creator credibility, and content quality metrics\n Own ML infrastructure decisions: choosing the right tradeoffs between batch, near-real-time, and streaming serving architectures\n \n What we're looking for \n \n 3-5+ years of experience building production recommendation or ranking systems, ideally in a UGC, marketplace, or content discovery context\n Experience with deep learning for information retrieval and multi-stage recommendation pipelines (candidate generation, scoring, reranking)\n Demonstrated ability to design and analyze A/B experiments, with awareness of biases inherent to recommendation systems\n Strong Python engineering skills with experience in PyTorch and large-scale data processing frameworks (Spark preferred)\n Comfort working in a cloud-based ML environment\n Experience with explore/exploit strategies, content cold-start, or counterfactual evaluation methods applied to recommendation\n Experience with content understanding models (NLP, vision, or generative AI) used as ranking features\n Familiarity with creator economy dynamics and how recommendation design affects content quality and creator incentives\n Experience with our stack: PyTorch (TorchRec, Transformers), Ray, Databricks, AWS\n Passion for video games and/or experience with gaming analytics\n \n This role is open to multiple locations across the US (including CA, NYC, \u0026 WA). \n EPIC JOB + EPIC BENEFITS = EPIC LIFE\n Our intent is to cover all things that are medically necessary and improve the quality of life. We pay 100% of the premiums for both you and your dependents. Our coverage includes Medical, Dental, a Vision HRA, Long Term Disability, Life Insurance \u0026 a 401k with competitive match. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees \u0026 dependents. Throughout the year we celebrate our employees with events and company-wide paid breaks. We offer unlimited PTO and sick time and recognize individuals for 7 years of employment with a paid sabbatical. \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 $184,481 — $270,571 USD \n California Base Pay Range\n $162,343 — $238,103 USD \n Washington Base Pay Range\n $147,584 — $216,457 USD \n ABOUT US\n Epic Ga","salary_min":147584,"salary_max":216457,"location":"BLANK,BLANK,Multiple Locations","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["search","deep-learning","pytorch","generative-ai","nlp","machine-learning"],"apply_url":"https://epicgames.com/careers/jobs/5973992004?gh_jid=5973992004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T23:49:51Z","expires_at":"2026-05-19T14:22:41.152306Z","created_at":"2026-04-19T14:22:41.2196Z","updated_at":"2026-04-19T14:22:41.2196Z","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/0a14bfc3-7578-44dc-b18d-c0b4bc052b6d"},{"id":"77733a7a-429e-484b-9bf4-05d70c323af3","company_id":"9f42c3ea-cd86-472e-8b5e-d041b53f16bf","title":"Machine Learning Engineer II (Fraud) ","slug":"machine-learning-engineer-ii-fraud-95838ad7","description":"Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.\n On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve.\n  \n What you’ll do \n - You will develop and iterate on fraud prediction models using a mix of approaches for tabular and behavioral data\n - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.\n - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.\n - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.\n - You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.\n - You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.\n  \n What we look for \n - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field.\n - Strong Python skills and experience writing production-quality code.\n - Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar).\n - Experience with a deep learning framework (PyTorch preferred).\n - Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar).\n - Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).\n - Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.\n - You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.\n - You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.\n - Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.\n - You have strong verbal and written communication skills that support effective collaboration with our global engineering team. \n Pay Grade - L Equity Grade - 5 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.  Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company). CAN base pay range per year: $125,000 - $175,000 #LI Remote\n \n Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.\n We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:  \n \n Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents  \n Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses \n Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge \n ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount \n \n We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to pr","salary_min":125000,"salary_max":175000,"location":"Remote (Canada)","workplace":"remote","job_type":"full-time","experience_level":"junior","tags":["mlops","deep-learning","pytorch","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/affirm/jobs/7695818003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T23:21:26Z","expires_at":"2026-05-19T14:23:51.114225Z","created_at":"2026-04-19T14:23:51.176711Z","updated_at":"2026-04-19T14:23:51.176711Z","company_name":"Affirm","company_slug":"affirm","company_logo_url":"https://www.google.com/s2/favicons?domain=affirm.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/77733a7a-429e-484b-9bf4-05d70c323af3"},{"id":"01850049-3d72-45b8-b0cf-51030a889893","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Safeguards Labs","slug":"research-engineer-safeguards-labs-2dfef8d5","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 Team \n Safeguards Labs is a new team operating at the intersection of research and engineering, chartered to investigate novel safety methods that protect Claude and the people who use it. We prototype new approaches to safe models, usage safeguards, and production safety — pressure-testing ideas through offline analysis and subsets of traffic before they graduate into production systems run by our partner Safeguards teams. Our work overlaps closely with account abuse, model behavior safeguards, and other safeguard subteams, and we serve as a research arm that can take on ambitious, ambiguous problems and turn them into deployed defenses.\n About the Role \n We're hiring research engineers to define and execute the Labs research agenda. You'll scope your own projects, run experiments end-to-end, and decide when an idea is ready to hand off to a production team — or when to kill it and move on. The team is small and being built deliberately around a roughly 3:1 mix of researchers to software engineers, so each person has substantial latitude over what they work on and high leverage on the team's direction.\n Responsibilities: \n \n Lead and contribute to research projects investigating new methods for detecting misuse of Claude, identifying malicious organizations and accounts, strengthening model safeguards, and other safety needs.\n Design and run offline analyses over model usage data to surface abuse patterns, build classifiers and detection systems, and evaluate their effectiveness.\n Develop and iterate on prototypes that could eventually feed signals into the real-time safeguards path, partnering with engineers on tech transfer.\n Contribute to a broader research portfolio investigating methods for detecting abusive behavior in chat-based or agentive workflows, and for training the model to robustly refrain from dangerous responses or behaviors without over-refusing.\n Build evaluations and methodologies for measuring whether safeguards actually work, including in agentic settings.\n Write up findings clearly so they inform decisions across Trust \u0026 Safety, research, and product teams.\n \n You may be a good fit if you: \n \n Have a track record of independently driving research projects from ambiguous problem statements to concrete results, ideally in AI, ML, security, integrity, or a related technical field.\n Are comfortable scoping your own work and switching between research, engineering, and analysis as a project demands.\n Have working familiarity with how large language models operate — sampling, prompting, training — even if LLMs aren't your primary background.\n Are proficient in Python and comfortable working with large datasets.\n Care about the societal impacts of AI and want your work to directly reduce real-world harm.\n \n Strong candidates may also have:  \n \n Experience building and training machine learning models, including classifiers for abuse, fraud, integrity, or security applications.\n Knowledge of evaluation methodologies for language models and experience designing evals.\n Experience with agentic environments and evaluating model behavior in them.\n Background in trust and safety, integrity, fraud detection, threat intelligence, or adversarial ML.\n Experience with red teaming, jailbreak research, or interpretability methods like steering vectors.\n A history of taking research prototypes and transferring them into production systems.\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 $350,000 — $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 you meet every single qualification.","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["agents","search","llm","alignment","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5191785008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T22:47:06Z","expires_at":"2026-05-19T14:00:28.74241Z","created_at":"2026-04-19T14:00:28.817023Z","updated_at":"2026-04-19T14:00:28.817023Z","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/01850049-3d72-45b8-b0cf-51030a889893"},{"id":"e9c82a7e-6ed6-4870-9bfb-52d6993aa2ab","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior/Staff Research Scientist, Frontier Benchmarks","slug":"seniorstaff-research-scientist-frontier-benchmarks-008357e1","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n ABOUT THE ROLE  \n We're looking for a Staff or Senior Research Scientist to collaborate with partners and lead the development of the next frontier benchmarks and datasets. This is a highly visible, customer-facing role at the intersection of research, company strategy, and go-to-market. You'll design datasets taking into account frontier model performance and work with our academic partners, and then partner with delivery, product and go-to-market to scale out production. You will also  serve as a credible technical partner for our customers, prospects, and drive results that impact the broader research community. \n This role reports directly to the Head of Research and is ideal for someone who is energized by cross-functional work and wants to understand how startups operate across research, data operations, and commercial teams. \n MAIN RESPONSIBILITIES  \n \n Design state of the art datasets that drive frontier model training and evaluation based on current model performance and academic partnerships \n Translate benchmark insights into clear, compelling narratives that articulate the ROI of expert-curated data for customer-facing presentations, technical reports, and go-to-market materials.\n Work cross-functionally with data operations, product, engineering, and strategy to surface research findings that inform the company roadmap. \n Stay at the frontier of LLM evaluation research and bring best practices into Snorkel's workflows\n Represent Snorkel's research externally through publications, blog posts, conference talks, and customer engagements that advance the conversation around data-centric AI\n \n PREFERRED QUALIFICATIONS  \n \n Strong research background in AI/ML evaluation, NLP, or related fields, with a track record of rigorous experimental design — especially around measuring the impact of training and evaluation data on model behavior. \n Exceptional communication skills — able to present complex technical findings clearly to both technical and non-technical audiences \n Comfort operating in a fast-moving, cross-functional environment with ambiguous problem spaces \n Genuine interest in GTM strategy, startup dynamics, and the commercial side of AI data services. \n Ph.D. in machine learning, NLP, or a related field preferred; equivalent industry or research lab experience considered.\n \n  \n Salary Range \n $220,000 — $320,000 USD \n Be Your Best at Snorkel \n Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.\n Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need. \n We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.","salary_min":220000,"salary_max":320000,"location":"Redwood City, CA","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","llm","nlp","research"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/5973937004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T22:29:50Z","expires_at":"2026-05-19T14:03:25.70727Z","created_at":"2026-04-19T14:03:25.777182Z","updated_at":"2026-04-19T14:03:25.777182Z","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/e9c82a7e-6ed6-4870-9bfb-52d6993aa2ab"},{"id":"7a18f49d-8558-4f6e-8d9c-60bb23c887e2","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Research Scientist, RL Training","slug":"research-scientist-rl-training-3f4f91c9","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n ABOUT THE ROLE  \n We're looking for a Research Scientist to work on reinforcement learning for training and aligning large language models. This is a foundational research role focused on one of the most consequential open data problems in AI: how to generate the data, reward signals, and training procedures that steer LLM behavior in reliable and generalizable directions — and a core capability that directly differentiates Snorkel's data-as-a-service offering. \n You'll work closely with Snorkel's research, engineering, and delivery teams to advance our RL data capabilities — translating research ideas into the preference datasets, reward models, and RL-ready corpora we produce for frontier AI labs, and contributing to a research agenda that is central to Snorkel's long-term differentiation as a provider of bespoke training data. \n MAIN RESPONSIBILITIES  \n \n Research and implement reinforcement learning techniques — including GRPO, RLHF, RLAIF, DPO, and reward modeling — and translate them into data products (preference datasets, reward signals, verifiable rewards) that customers can use to train and fine-tune large language models. \n Design and build data pipelines that generate high-quality training signal for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks . \n Prototype and iterate on end-to-end RL training recipes that inform what data Snorkel ships as part of its data-as-a-service deliveries. \n Work closely with research scientists, ML engineers, and delivery teams to translate RL research into customer-ready data products.\n Stay current with the latest developments in large-scale muli-node LLM training, alignment research, and scalable RL methods (on complex environments such as Terminal-Bench), bringing relevant advances into Snorkel's data-as-a-service approach.\n Contribute to Snorkel's research publications and internal knowledge base in RL and model training.\n \n PREFERRED QUALIFICATIONS  \n \n Deep expertise in reinforcement learning from human or AI feedback, reward modeling and credit attribution ideally with a clear perspective on what data makes these techniques work. \n Experience training or fine-tuning 30B+ large language models at scale, including familiarity with distributed training infrastructure. \n Strong proficiency in Python and ML frameworks, especially PyTorch and HuggingFace and hands-on experience with RL frameworks such as Verl and SkyRL. \n Solid software engineering fundamentals — you can build research prototypes that others can run, extend, and integrate into data production workflows. \n Familiarity with ML infrastructure and cloud platforms and tools (AWS, GCP, Kubernetes, Slurm, etc.); experience with large-scale RL training pipelines a strong plus. \n Comfort operating in a high-iteration environment with open-ended research questions and shifting, customer-driven technical constraints. \n Ph.D. in machine learning, reinforcement learning, or a related field strongly preferred; exceptional industry experience considered. \n Salary Range \n $200,000 — $275,000 USD \n Be Your Best at Snorkel \n Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.\n Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and haras","salary_min":200000,"salary_max":275000,"location":"Redwood City, CA","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","distributed-systems","pytorch","fine-tuning","alignment","data-pipeline","reinforcement-learning","llm"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/5973944004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T22:29:49Z","expires_at":"2026-05-19T14:03:24.838278Z","created_at":"2026-04-19T14:03:24.909949Z","updated_at":"2026-04-19T14:03:24.909949Z","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/7a18f49d-8558-4f6e-8d9c-60bb23c887e2"},{"id":"d2677e68-49e3-4b42-ac40-33d589a94c04","company_id":"0bedcaf4-210e-4f52-95d5-a82be8aff446","title":"Staff AI Security Engineer","slug":"staff-ai-security-engineer-166abf55","description":"Cribl does differently. \n What does that mean? It means we are a serious company that doesn’t take itself too seriously; and we’re looking for people who love to get stuff done, and laugh a bit along the way. We’re growing rapidly - looking for collaborative, curious, and motivated team members who are passionate about putting customers first. As a remote-first company we believe in empowering our employees to do their best work, wherever they are. \n As the data engine for IT and Security many of the biggest names in the most demanding industries trust Cribl to solve their most pressing data needs. Ready to do the best work of your career? Join the herd and unlock your opportunity.\n Why You’ll Love This Role \n ​We are seeking a talented and experienced Staff AI Security Engineer to help build Cribl’s new AI Systems team. In this pivotal role, you will design, implement, and operationalize security and governance frameworks that enable rapid AI adoption to scale safely across Cribl’s internal systems and workflows. This is a foundational role on a newly established team tasked with providing the shared infrastructure, security guardrails, and reusable patterns needed to turn AI from fragmented experimentation into durable company capabilities.\n You will be instrumental in bringing security, governance, and safety to Cribl’s rapidly expanding AI footprint, including API tokens, secrets management, MCP security, shadow AI mitigation, AI telemetry, and compliance readiness. The team’s mandate is to provide the “paved road” for AI at Cribl: secure access, governed integrations, reusable workflows, and a platform that enables teams to move faster without creating security, compliance, or operational risk.\n This role will be part of the Corporate AI Systems team and will report directly to the Chief Information Security Officer (CISO). It will partner closely with stakeholders across Security, Enterprise Applications, Product, Engineering, IT, Legal, and the various business teams adopting AI to ensure Cribl’s AI capabilities scale securely and pragmatically.\n As An Active Member Of Our Team, You Will... \n As the Staff AI Security Engineer, you will be the foundational builder of Cribl’s AI security and governance layer. Your key responsibilities will include:\n \n AI Security Architecture \u0026 Governance: Define, threat model, and operationalize the security architecture for Cribl’s internal AI platform, including standards, controls, approval patterns, and secure-by-design guidance for AI use cases before they scale into production.\n Shadow AI Discovery \u0026 Remediation: Partner with Business Operations to maintain visibility into AI tools, licenses, API tokens, MCP servers, and ad hoc workflows in use across the company, and monitor for ungoverned or high-risk patterns that require remediation.\n MCP Security \u0026 Registry Management: Own the framework for vetting MCP servers, maintaining an approved registry, defining risk tiers, and enforcing secure connection patterns as MCP adoption expands across teams.\n Secrets, Identity \u0026 Token Protection: Establish secure patterns for secrets management, non-human identities, scoped credentials, OAuth-based access, and token governance to enforce least-privilege access and reduce credential exposure in AI builds.\n Prompt Injection Defense \u0026 Safe Execution Controls: Design and deploy guardrails for prompt injection defense, deterministic validation, human-in-the-loop approvals, and additional controls for high-risk workflows that combine sensitive data, untrusted content, and external action.\n AI Telemetry, Detection \u0026 Incident Response: Partner on building Cribl as the observability backbone for AI systems, including telemetry pipelines, abuse detection, audit trails, threat hunting, and incident response patterns for AI-specific security events.\n Compliance \u0026 Customer Governance Readiness: Partner with Cribl’s Compliance team to drive documentation and control readiness for AI-related obligations and customer scrutiny, including NIST AI RMF, ISO 42001, EU AI Act readiness, AI acceptable use standards, and customer-facing AI governance materials.\n Secure AI-Assisted Corporate Engineering Enablement: Establish the security controls required for AI-assisted internal development, secure coding practices, secrets management, SCA/SAST/DAST expectations, and review patterns for AI-generated code and workflows.\n Risk Metrics \u0026 Security Effectiveness: Define and track the metrics that matter most for AI security, including shadow AI exposure, control coverage, incident trends, security review turnaround, and reduction of high-risk patterns as the platform scales across the company.\n We are a remote-first company and work happens across many time-zones - you may be required to occasionally perform duties outside your standard working hours.\n \n If You’ve Got It - We Want It \n \n Staff-level security engineering experience: 7+ years of experience in security e","salary_min":128000,"salary_max":200000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["agents","security","llm","cloud"],"apply_url":"https://cribl.io/job-detail/?gh_jid=5973782004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T20:19:10Z","expires_at":"2026-05-19T14:23:59.080061Z","created_at":"2026-04-19T14:23:59.149803Z","updated_at":"2026-04-19T14:23:59.149803Z","company_name":"Cribl","company_slug":"cribl","company_logo_url":"https://www.google.com/s2/favicons?domain=cribl.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d2677e68-49e3-4b42-ac40-33d589a94c04"},{"id":"94c84ee0-7989-495d-8a56-f7213500f10e","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer, Prediction \u0026 Planning, System Architecture","slug":"senior-machine-learning-engineer-prediction-planning-system-architecture-1a2fab11","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently.\n The system architecture team handles the onboard contract of the model with the system, including kinematics, interfaces and representations. Our team’s mission is to work across the stack, building the best setup for the model to drive. We tackle this through projects in data, modeling, metrics, and the overall planner system.\n In this hybrid role, you will report to a Technical Lead Manager.  \n You will: \n \n Tackle challenging real-world problems with ML and engineering solutions.\n Use state of the art techniques to design and build ML models, deploy them in the real world on production vehicles\n Design and build the necessary architectures, algorithms, pipelines and evaluation systems on Google's extensive data infrastructure\n Collaborate with researchers, product area owners and engineers to develop safe, smooth planning behavior for all road users \u0026 deliver product requirements.\n \n You have: \n \n Bachelors in Computer Science, ML, Robotics, similar technical field of study, or equivalent practical experience\n Hands-on experience with modern deep learning libraries (eg: TensorFlow, JAX, Pytorch) \n Proficient programming skills (eg: Python, C/C++)\n Strong analytical and problem solving skills\n 4+ years of experience in Machine Learning modeling and/or Autonomous Vehicles systems\n \n We prefer: \n \n MS or PhD in Computer Science, Machine Learning, Robotics, or a related field\n Publications in top-tier conferences such as ICML, NeurIPS,  CVPR, ICCV, ECCV, ICLR, IROS, CoRL, ACL, or EMNLP\n Prior software development or ML research industry experience (including internships)\n General software engineering experience solving motion planning or related robotics problems\n Experience applying or evaluating ML-based systems in production environments\n Experience with performance optimization of deep models, including with respect to specific hardware architectures\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $212,500 — $270,000 USD","salary_min":212500,"salary_max":270000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["deep-learning","tensorflow","nlp","robotics","pytorch","autonomous-vehicles","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7826591","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T19:21:47Z","expires_at":"2026-05-19T14:04:50.66452Z","created_at":"2026-04-17T19:31:59.158595Z","updated_at":"2026-04-19T14:04:50.742876Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/94c84ee0-7989-495d-8a56-f7213500f10e"},{"id":"69885e5b-407d-42e8-b34d-e8a03a7126f6","company_id":"dcf03132-cd3a-4108-8e1d-20ab36008ea2","title":"Senior Data Scientist, West","slug":"senior-data-scientist-west-49ee9fab","description":"Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams design and operate analytics, machine learning, and AI agents with the transparency, collaboration, and control enterprises require. Sitting above data platforms, cloud infrastructure, and AI services, Dataiku connects the full enterprise AI stack — empowering organizations to run AI across multi-vendor environments with centralized governance. The world’s leading companies rely on Dataiku to operationalize AI and run it as a true business performance engine delivering measurable value. For more, visit the Dataiku blog , LinkedIn , X , and YouTube .\n The role of a Senior Data Scientist at Dataiku is quite unique. Our Data Scientists not only code up solutions to real-world problems but also participate in client-facing endeavors throughout the customer journey. This includes supporting their discovery of the platform, helping integrate Dataiku with other tools and technologies, some user training, and co-developing data science projects from design to deployment.Just as the non-technical skills are important, so too are the technical. Our Data Scientists work on the Dataiku platform every day. Aside from the visual tools, our team uses mostly Python and SQL, with occasional work in other languages (e.g., R, Pyspark, JavaScript, etc.). An ideal candidate is excited to learn complex new technologies and modeling techniques while being able to explain their work to other data scientists and clients.\n Key Areas of Responsibility (What You’ll Do) \n \n Scope and co-develop production-level data science projects with our customers across different industries and use cases\n Help users discover and master the Dataiku platform via user training, office hours, and ongoing consultative support\n Provide strategic input to the customer and account teams that help make our customers successful.\n Provide data science expertise both to customers and internally to Dataiku’s sales and marketing teams\n Lead technical data science projects pre-sales scoping and design appealing proposals\n Flag technical and non-technical account risks (onboarding issues, performance pitfalls, timeline slippage)\n Develop custom Python-based “plugins” in collaboration with Solutions, R\u0026D, and Product teams, to enhance Dataiku’s functionality\n  Lead Data Scientist engagements: You will coordinate agile sprints, prioritize tasks, estimate effort, do backlog grooming\n Run demo booth/tech talk duties at company public events (e.g. Everyday AI)\n Lead Junior Data Scientist technical interview\n Contribute to 2 internal assets (internal best practice or external blog post/project on the public gallery) per year\n \n Experience (What We’re Looking For): \n \n Curiosity and a desire to learn new topics and skills\n Empathy for others and an eagerness to share your knowledge and expertise with your colleagues, Dataiku’s customers, and the general public\n The ability to clearly explain complex topics to technical as well as non-technical audiences\n Over 5 years of experience with Python and SQL\n Over 5 years of experience with building ML models and using ML tools (e.g., sklearn)\n Experience with LLMs\n Experience with data visualization and building web apps with Python frameworks (Dash, Streamlit)\n Understanding of underlying data systems such as Cloud architectures and SQL\n Bachelor’s or Master’s program focused on: Statistics, Computer Science, or a related field\n Location: Must be located within the CST, MST, and PST time zones \n \n Bonus points for any of these: \n \n Experience using Dataiku DSS\n Experience with Consulting and/or Customer-facing Data Science roles\n Experience in the Manufacture \u0026 Defense sector\n Experience with Data Engineering or MLOps\n Experience developing WebApps in Javascript\n Experience building APIs\n Passion for teaching or public speaking\n Compensation and Benefits \n The final compensation package for this role will be determined during the interview process and is based on a variety of factors, including, but not limited to, geographic location, internal equity, education, skill set, experience and training. Eligible roles may also be entitled to receive commission or other variable compensation through Dataiku's incentive compensation program.  \n Dataiku also offers comprehensive benefits, including stock options, medical, dental, and vision plans, flexible spending accounts, pre-tax commuter benefits, a 401k company match, paid vacations and sick leave, paid parental leave, employer paid disability coverage, and additional health and wellbeing perks and benefits. Dataiku reserves the right to amend or modify employee perks and benefits at any time. \n US only national base pay ranges\n $170,000 — $190,000 USD \n  \n What are you waiting for! \n At Dataiku, you'll be part of a journey to shape the ever-evolving world of AI. We're not just building a product; we're cr","salary_min":170000,"salary_max":190000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["agents","cloud","mlops","llm","payments","data-science"],"apply_url":"https://job-boards.greenhouse.io/dataiku/jobs/5973402004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T19:08:47Z","expires_at":"2026-05-19T14:10:01.348781Z","created_at":"2026-04-17T19:36:38.584429Z","updated_at":"2026-04-19T14:10:01.422857Z","company_name":"Dataiku","company_slug":"dataiku","company_logo_url":"https://www.google.com/s2/favicons?domain=dataiku.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/69885e5b-407d-42e8-b34d-e8a03a7126f6"},{"id":"200e5f23-8158-4eb8-ab5c-d40e414f8efb","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer (Infra), Driver Understanding and Evaluation","slug":"senior-machine-learning-engineer-infra-driver-understanding-and-evaluation-da1f274f","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack.\n You will: \n \n Build scalable systems for training and fine-tuning large-scale models to evaluate interesting driving behaviors.\n Work at the intersection of data engineering, model development, and simulation Provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives.\n Contribute to the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions of miles.\n Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation.\n Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.\n \n You have: \n \n M.S. or Ph.D. degree Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.\n 5+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.\n A history of contributions to machine learning tooling and frameworks e.g. PyTorch, Jax, Tensorflow, Ray, or similar. The candidate should understand both the user facing API and the internal workings. \n Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.\n \n We prefer: \n \n 7+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.\n Experience in the autonomous vehicles domain, robotics, or complex simulation environments.\n Familiarity with large-scale simulation platforms and their integration with ML training workflows.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["robotics","pytorch","fine-tuning","distributed-systems","tensorflow","autonomous-vehicles","evaluation","infrastructure"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7819951","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T19:03:21Z","expires_at":"2026-05-19T14:04:50.409115Z","created_at":"2026-04-17T19:31:58.91374Z","updated_at":"2026-04-19T14:04:50.483834Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/200e5f23-8158-4eb8-ab5c-d40e414f8efb"},{"id":"f493bf3a-6b0a-46c6-b687-b12b2ab2a0ee","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Machine Learning Engineer (Infra), Driver Understanding and Evaluation","slug":"machine-learning-engineer-infra-driver-understanding-and-evaluation-c3118e30","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack.\n You will: \n \n Build scalable systems for training and fine-tuning large-scale models to evaluate interesting driving behaviors.\n Work at the intersection of data engineering, model development, and simulation Provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives.\n Contribute to the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions of miles.\n Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation.\n Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.\n \n You have: \n \n M.S. or Ph.D. degree Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.\n 3+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.\n A history of contributions to machine learning tooling and frameworks e.g. PyTorch, Jax, Tensorflow, Ray, or similar. The candidate should understand both the user facing API and the internal workings. \n Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.\n \n We prefer: \n \n 5+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.\n Experience in the autonomous vehicles domain, robotics, or complex simulation environments.\n Familiarity with large-scale simulation platforms and their integration with ML training workflows.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $170,000 — $216,000 USD","salary_min":170000,"salary_max":216000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","robotics","fine-tuning","tensorflow","autonomous-vehicles","pytorch","infrastructure","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7819946","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T19:03:14Z","expires_at":"2026-05-19T14:04:48.128547Z","created_at":"2026-04-17T19:31:56.8697Z","updated_at":"2026-04-19T14:04:48.203226Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f493bf3a-6b0a-46c6-b687-b12b2ab2a0ee"},{"id":"e9e1380d-f448-4b09-91ea-e014b4632db9","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"Applied Scientist","slug":"applied-scientist-3a044b49","description":"About Dialpad Dialpad is the AI-native business communications platform. We unify calling, messaging, meetings, and contact center on a single platform - powered by AI that understands every conversation in real time. \n More than 70,000 companies around the globe, including WeWork, Asana, NASDAQ, AAA Insurance, COMPASS Realty, Uber, Randstad, and Tractor Supply, rely on Dialpad to build stronger customer connections using real-time, AI-driven insights. \n We’re now leading the shift to Agentic AI: intelligent agents that don’t just analyze conversations but take action by automating workflows, resolving customer issues, and accelerating revenue in real time. Our DAART initiative (Dialpad Agentic AI in Real Time) is redefining what a communications platform can do. \n Visit dialpad.com to learn more. \n Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. \n We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. \n We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . \n Your role As an Applied Scientist at Dialpad, you'll be an integral part of our AI team, conducting R\u0026D to power the next generation of autonomous voice agents and delivering features for transcribed voice and chat message data in the business communications domain. We have several research themes, including developing multi-modal, real-time agentic systems that can listen, reason, and take action during live customer interactions. We are also developing real-time knowledge retrieval models to power live coaching features for customer support and sales agents. Beyond the technical skills, we are a team that values collaboration, continuous learning, and the application of diverse perspectives to solve complex problems. Collaboration will be key as you work alongside our engineering, design, and product teams to build groundbreaking applications. \n If you're passionate about language, AI, and contributing to a team that's changing the face of business communications, you'll find yourself right at home with us. \n This position reports to the Manager of the NLP team and has the opportunity to be based in our Kitchener, ON, office. \n What you’ll do \n \n Develop, implement, and refine state-of-the-art Natural Language Processing and Machine Learning algorithms for Dialpad's products. \n Conduct rigorous evaluation and monitoring of model performances and troubleshoot issues with a keen understanding of resultant business impacts. \n Manage massive textual data sets. \n Build advanced LLM-based features, including reasoning, multilingual and multimodal processing, and agents. \n Collaborate with cross-functional teams, including engineering, product, and design, to effectively deploy and scale models and algorithms in production. \n Submit papers to top-tier academic conferences and journals and contribute to the broader scientific community by reviewing submissions. \n \n Skills you’ll bring \n \n Master’s or PhD degree in Linguistics, Computational Linguistics, Computer Science, Machine Learning, or related fields. \n 2+ years of NLP industry experience for Master’s degree holders or 1+ years for PhD degree holders. \n Demonstrated experience with machine learning, Python, PyTorch, and other relevant tools and technologies. \n A broad understanding of current LLM model architectures and techniques for tuning and optimizing LLMs. \n Strong problem-solving and analytical abilities, with the capacity to handle complex technical and analytical problems. \n Excellent communication and collaboration skills to effectively work in a multi-disciplinary team.  \n Familiarity with version control tools like Git for collaborative projects. \n For exceptional talent based in Ontario, Canada  the target base salary range for this position is posted below. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the target range for new hire salaries for the position. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in Ontario role postings reflect the base salary only, and do not include bonus, equity, or benefits. \n Ontario Pay Transparency Range\n $145,500 — $172,500 CAD \n Why Join Dialpad \n \n Work at the center of the AI transformation in busine","salary_min":145500,"salary_max":172500,"location":"Kitchener, Canada","workplace":"onsite","job_type":"full-time","experience_level":"junior","tags":["agents","rag","nlp","pytorch","llm"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8512128002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T18:49:54Z","expires_at":"2026-05-19T14:25:42.200151Z","created_at":"2026-04-17T19:52:23.600414Z","updated_at":"2026-04-19T14:25:42.267307Z","company_name":"Dialpad","company_slug":"dialpad","company_logo_url":"https://www.google.com/s2/favicons?domain=dialpad.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e9e1380d-f448-4b09-91ea-e014b4632db9"},{"id":"0dcc2e8e-a8cd-449a-9eae-c3c6916b5b85","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Multimodal Understanding","slug":"member-of-technical-staff-multimodal-understanding-d5a4390f","description":"About xAI \n xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \n ABOUT THE ROLE: \n You will join the multimodal team to push toward superhuman multimodal intelligence. Advance understanding and generation across modalities—image, video, audio, and text—spanning the full stack: data curation/acquisition, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences.\n Collaborate cross-functionally with pre-training, post-training, reasoning, data, applied, and product teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration. Contribute to building models that can see, hear, reason about, and interact with the world in real time at unprecedented levels.\n RESPONSIBILITIES: \n \n Design, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale.\n Develop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text).\n Advance multimodal capabilities including spatial-temporal compression, cross-modal alignment, world modeling, reasoning, emergent abilities, audio/image/video understanding \u0026 generation, real-time video processing, and noisy data handling.\n Drive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models.\n Create evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy.\n Innovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance.\n Build research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback.\n Work across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions.\n \n BASIC QUALIFICATIONS: \n \n Hands-on experience with multimodal pre-training, post-training, or fine-tuning (vision, audio, video, or cross-modal).\n Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA.\n Proven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design).\n Deep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data.\n Strong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors).\n Proactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers.\n Willingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences.\n \n PREFERRED SKILLS AND EXPERIENCE:\n \n Experience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling.\n Familiarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems.\n Proficiency in Rust and/or C++ for performance-critical components.\n Hands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes.\n Background building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership.\n Passion for end-to-end user experience in interactive, real-time multimodal AI systems.\n \n COMPENSATION AND BENEFITS: \n $180,000 - $440,000 USD\n Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n xAI is an equal opportunity employer. For details on data processing, view our  Recr","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pre-training","reinforcement-learning","generative-ai","llm","data-pipeline","fine-tuning","agents","pytorch"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5111374007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T18:05:41Z","expires_at":"2026-05-19T14:03:17.610073Z","created_at":"2026-04-17T19:30:23.022034Z","updated_at":"2026-04-19T14:03:17.685978Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0dcc2e8e-a8cd-449a-9eae-c3c6916b5b85"},{"id":"8afd3a49-111b-47b6-8316-1d7a37c98911","company_id":"86d1902d-10d3-40a4-b92e-cd8cb5e106b9","title":"Deep Learning Engineer ","slug":"deep-learning-engineer-ee3f32bc","description":"The Carbon Robotics LaserWeeder™ leverages advanced robotics, computer vision, AI/deep learning, and lasers to eliminate weeds with sub-millimeter accuracy—all without herbicides. This innovative solution reduces environmental impact, promotes soil health, and helps farmers address labor shortages and rising costs. Designed in Seattle and built at our cutting-edge manufacturing facility in Richland, Washington, the LaserWeeder is setting a new standard for automated weed control. With $157 million in funding from prominent investors such as BOND, NVentures (NVIDIA’s venture arm), Anthos Capital, Fuse Venture Capital, Ignition Partners, Revolution, Sozo Ventures, and Voyager Capital , Carbon Robotics is driving innovation.\n As a no-nonsense team with a bias for action, we take pride in executing our ideas. Whether it’s designing transformative technology or visiting farms to ensure our products are reliable and safe, we do whatever it takes to deliver for our customers. \n Working here means tackling big problems with big impact. You’ll find opportunities to grow professionally, solve complex challenges, and make meaningful contributions to a mission that matters. At Carbon Robotics, we trust our team to act independently and make practical, real-world decisions. \n Join us as we innovate, execute, and build the future of farming together.\n YouTube | X | Instagram | LinkedIn | News \n Deep Learning Engineer  \n As a Deep Learning Engineer at Carbon Robotics, you will contribute to designing, developing, and deploying novel deep learning systems that power our autonomous laser weeding robots in the field. \n What You'll Do \n \n Lead the design and execution of experiments to develop and validate novel deep learning architectures for computer vision in agricultural environments\n Own model optimization and deployment pipelines — ensuring high performance, reliability, and scalability across operational field deployments\n Drive end-to-end ML workflows from data strategy and pipeline design through evaluation and production deployment\n Define best practices for experimentation, documentation, and model evaluation within the team\n Partner with Engineering and Product Management to scope, prioritize, and deliver high-impact features\n Mentor and provide technical guidance to mid-level and junior engineers\n Communicate model architecture decisions, tradeoffs, and performance results to both technical and non-technical audiences\n \n Knowledge, Skills \u0026 Abilities \n \n 2-4 years of professional experience designing and implementing novel deep learning architectures for production computer vision systems\n Deep understanding of foundational deep learning mathematics and the ability to apply first-principles thinking to architecture decisions\n Hands-on experience working across the software stack, including sensor integration and web services, ideally within a robotics or autonomous field equipment platform\n Experience with deep learning frameworks, particularly PyTorch, and proficiency in C++ for performance-critical model development and deployment\n Proven track record taking ML projects from inception through business impact — including data strategy, pipeline development, experimentation, and deployment at scale\n Strong expertise in modern object detection techniques (vision transformers, anchor-free detectors, embeddings, and beyond)\n Experience in autonomous driving or ADAS is a plus — background in perception pipelines, sensor fusion, or real-time inference in outdoor or unstructured environments is highly valued\n Comfort navigating ambiguity and making principled technical decisions in rapidly evolving technical landscapes\n Strong verbal and written communication skills — able to explain complex model behavior and tradeoffs to non-technical staff and customers\n Experience mentoring engineers and contributing to team technical culture\n \n Requirements \n \n 2-7 years of experience in deep learning model optimization and deployment\n BS+ in Computer Science, Machine Learning, or a related field (or equivalent experience)\n \n In Office Requirements \n \n We're a collaborative, in-person team — this role is based in our Seattle office with at least 4 days per week on-site\n \n  \n Carbon Robotics follows equitable hiring practices. Flexibility in our hiring process allows hiring of talent at levels different from what are posted.  The compensation range outlined is based on a target budgeted base salary. Individual base pay depends on various factors such as relevant experience and skill, Interview assessments and responsibility of role, job duties/requirements. Offers are determined using our equitable hiring practices. Carbon Robotics offers additional compensation in the form of benefits premiums, pre-IPO stock options and On Target Earning commissions for appropriate positions. Base pay ranges are reviewed each year. We are committed to the principle of pay equity – paying employees equitably for similar ","salary_min":140000,"salary_max":220000,"location":"Seattle, WA","workplace":"onsite","job_type":"full-time","experience_level":"junior","tags":["deep-learning","autonomous-vehicles","pytorch","computer-vision","healthcare","robotics"],"apply_url":"https://carbonrobotics.com/job-openings?gh_jid=4673637006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T16:22:22Z","expires_at":"2026-05-19T14:24:54.191314Z","created_at":"2026-04-17T19:51:30.145338Z","updated_at":"2026-04-19T14:24:54.273699Z","company_name":"Carbon Robotics","company_slug":"carbon-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=carbonrobotics.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8afd3a49-111b-47b6-8316-1d7a37c98911"},{"id":"5da2f5d6-dc6b-40ab-8831-cdadc09e2689","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"Applied Scientist","slug":"applied-scientist-2bfa52bd","description":"About Dialpad Dialpad is the AI-native business communications platform. We unify calling, messaging, meetings, and contact center on a single platform - powered by AI that understands every conversation in real time. \n More than 70,000 companies around the globe, including WeWork, Asana, NASDAQ, AAA Insurance, COMPASS Realty, Uber, Randstad, and Tractor Supply, rely on Dialpad to build stronger customer connections using real-time, AI-driven insights. \n We’re now leading the shift to Agentic AI: intelligent agents that don’t just analyze conversations but take action by automating workflows, resolving customer issues, and accelerating revenue in real time. Our DAART initiative (Dialpad Agentic AI in Real Time) is redefining what a communications platform can do. \n Visit dialpad.com to learn more. \n Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. \n We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. \n We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . \n Your role As an Applied Scientist at Dialpad, you'll be a key driver within our AI team, conducting R\u0026D to power the next generation of autonomous voice agents. While traditional NLP focuses on analyzing static text, your work will center on real-time, multimodal systems that can listen, reason, and take action during live customer interactions. A major focus of the team is advancing DialpadGPT, our proprietary LLM, specifically optimizing it to drive orchestrated agentic workflows and handle complex task execution. Beyond the technical skills, we are a team that values collaboration, continuous learning, and the application of diverse perspectives to solve complex problems. Collaboration will be key as you work alongside our engineering, design, and product teams to build groundbreaking agentic applications. \n If you're passionate about agentic and multimodal AI and contributing to a team that's changing the face of business communications, you'll find yourself right at home with us. \n This position reports to the Senior Manager of the NLP team and has the opportunity to be based in Vancouver, BC. \n What you’ll do \n \n Research and develop state-of-the-art algorithms for autonomous voice agents, specifically focusing on real-time speech processing and reasoning loops. \n Advance DialpadGPT: Design and execute distributed training strategies to optimize our proprietary LLMs for agentic behaviors, including precise tool use, instruction following, and latency-constrained generation. \n Conduct rigorous evaluation and monitoring of model performances and troubleshoot issues with a keen understanding of resultant business impacts. \n Design and implement orchestration layers that effectively chain LLMs with external tools and APIs to solve complex customer problems autonomously. \n Work with large-scale multimodal datasets (text, audio) to improve model robustness and alignment. \n Collaborate with engineering, product, and design teams to deploy scalable, low-latency models and algorithms in production. \n Submit papers to top-tier academic conferences (ACL, EMNLP, NeurIPS) and contribute to the team’s research culture. \n \n Skills you’ll bring \n \n Master’s or PhD degree in Computer Science, Machine Learning, Computational Linguistics, or a related quantitative field. \n 2+ years of industry experience in Machine Learning/NLP for Master’s degree holders, or 1+ years for PhD holders. \n Deep understanding of LLMs: Demonstrated experience with training, fine-tuning (PEFT/LoRA), and alignment techniques (RLHF/DPO) for specific domains or tasks. \n Experience with Agentic Systems: Familiarity with building autonomous agents, including concepts like tool use, function calling, reasoning chains (CoT), and memory management. \n Strong proficiency in Python and PyTorch, with the ability to write clean, production-ready research code. \n Research Track Record: A history of publishing in top-tier conferences (ACL, EMNLP, NeurIPS, ICASSP) is highly valued. \n Multimodal Awareness: Familiarity with speech technologies (ASR, TTS) or processing real-time audio streams is a strong plus. \n Ability to bridge the gap between research and product, translating complex technical concepts into business value. \n Familiarity with version control tools like Git for collaborative projects. \n For exceptional talent based in British Columbia, Canada  the target base salary range for this position is posted below. Our salary ranges are determined by role, level, and location. The range dis","salary_min":161500,"salary_max":191500,"location":"Vancouver, Canada","workplace":"onsite","job_type":"full-time","experience_level":"junior","tags":["agents","nlp","reinforcement-learning","pytorch","distributed-systems","generative-ai","llm","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8508615002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T15:47:47Z","expires_at":"2026-05-19T14:25:42.114618Z","created_at":"2026-04-17T19:52:23.244794Z","updated_at":"2026-04-19T14:25:42.187576Z","company_name":"Dialpad","company_slug":"dialpad","company_logo_url":"https://www.google.com/s2/favicons?domain=dialpad.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5da2f5d6-dc6b-40ab-8831-cdadc09e2689"},{"id":"8b7a3c7b-3f18-48d2-bad3-34bdf0847657","company_id":"a0000000-0000-0000-0000-000000000001","title":"Global Leader, Applied AI Architects, Beneficial Deployments","slug":"global-leader-applied-ai-architects-beneficial-deployments-c5f03476","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 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 Beneficial Deployments: \n Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences, focusing on raising the floor for the people and institutions working on humanity's hardest problems.\n About the Role: \n As the Global Leader of Applied AI Architects for Beneficial Deployments, you will lead the team of Applied AI Architects who serve as the primary technical partners to mission-driven organizations and non-profits adopting Claude. You'll build and scale a world-class, globally distributed team that turns frontier AI into real impact in education, global health, economic mobility, and life sciences—while shaping how Anthropic's most important societal partnerships are designed and delivered.\n You'll combine deep technical fluency with the leadership judgment needed to operate across segments, regions, and partner types—from global health foundations to leading research institutions to frontline nonprofits. You'll set the vision for how we scale our expertise from a handful of flagship partnerships to an ecosystem of organizations operating as AI-native, and you'll be accountable for the team, processes, and cross-functional relationships that make that possible.\n In collaboration with Beneficial Deployment’s Head of Nonprofits, Product, Engineering, Policy, and our broader GTM organization, you'll help ensure our partners incorporate Claude into their work responsibly, effectively, and in ways that meaningfully accelerate their missions. You'll represent Anthropic as a senior technical leader on some of our most visible and consequential partnerships, while maintaining our best-in-class safety standards.\n Responsibilities: \n \n \n Lead, grow, and mentor a globally distributed team of Architects supporting mission-driven non-profits across education, global health, economic mobility, and life sciences\n \n Set the vision, strategy, and operating model for how Applied AI shows up in Beneficial Deployments—from discovery through deployment, and from individual partnerships to ecosystem-wide infrastructure\n \n Establish hiring plans, team structure, and career development paths as we scale the team globally; set goals and reviews that promote growth, output, and a high bar for technical excellence\n \n Partner closely with segment leads and senior partner leadership to understand requirements and shape engagements on our highest-impact partnerships\n \n Drive the design of cohort-based accelerators, Claude Code enablement programs, and other scalable mechanisms that multiply our impact across many organizations simultaneously\n \n Identify patterns across partners and segments to inform what we build at the ecosystem level—MCPs, evals, reference implementations, and shared infrastructure\n \n Collaborate with Product and Engineering to surface partner needs, influence roadmap, and ensure learnings from the field shape how Claude evolves\n \n Represent Anthropic externally with senior leaders at foundations, nonprofits, research institutions, and government-adjacent organizations\n \n Travel to partner sites globally for workshops, technical deep dives, and relationship building\n \n Help shape team processes and culture as Beneficial Deployments scales, and contribute to the broader Applied AI leadership community at Anthropic\n \n Travel is 30-40% due to the global nature of the team (SF, NYC, London and Bengaluru) and events across Beneficial Deployments.\n \n You may be a good fit if you have: \n \n \n 10+ years of experience in technical, customer-facing roles (Solutions Architect, Forward Deployed Engineer, Customer Engineer, Sales Engineer, or similar), with meaningful exposure to complex, high-stakes deployments\n \n 7+ years of engineering or technical leadership experience, preferably building and scaling customer-facing or forward-deployed teams globally\n \n Experience working with or inside mission-driven organizations—education, healthcare, scientific research, global development, or nonprofits—and a genuine understanding of the constraints, incentives, and operating realities of these sectors\n \n Familiarity with common LLM implementation patterns, i","salary_min":315000,"salary_max":380000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["alignment","healthcare","agents","llm"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5192104008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T14:45:07Z","expires_at":"2026-05-19T14:00:21.324349Z","created_at":"2026-04-17T19:27:35.261302Z","updated_at":"2026-04-19T14:00:21.409929Z","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/8b7a3c7b-3f18-48d2-bad3-34bdf0847657"},{"id":"bd857cce-b68e-4773-a809-8d7611bf3956","company_id":"63bced38-3605-4e57-99f3-e213b2d40bf3","title":"Senior Forward-Deployed Engineer","slug":"senior-forward-deployed-engineer-5b64416e","description":"Opportunity Overview: \n We are seeking a Senior Forward-Deployed Engineer to join our Forward Deployed Engineering team at Cohere Health. In this highly technical, customer-facing role, you will own end-to-end technical integration work with enterprise customers and partners—from pre-sales through production deployment—bridging complex healthcare systems with Cohere’s AI-first platform. You’ll partner closely with Engineering, Product, Solutions Architecture, and Customer Success teams to design and execute sophisticated integrations that improve access to care for millions of members.\n What you’ll do: \n \n Own end-to-end technical integration design and execution for enterprise customers, from initial scoping through production deployment, serving as the primary technical authority throughout\n Lead technical discovery, scoping, and proof-of-concept work during pre-sales cycles, creating technical proposals and estimates to validate feasibility for prospective customers\n Design system architectures that bridge customer IT environments with Cohere’s platform, making key architectural decisions that balance customer constraints with platform capabilities\n Work hands-on with AI/ML-enabled healthcare features—including clinical decision support, automated review, and intelligent routing—ensuring they meet regulatory requirements and clinical safety standards\n Produce high-quality technical artifacts including integration designs, architecture diagrams, data mappings, API implementation guides, and operational runbooks\n Build reusable integration patterns and templates that scale across customers, and continuously identify opportunities to improve platform capabilities based on implementation learnings\n Collaborate cross-functionally with Engineering, Product, Solutions Architecture, and Customer Success teams to drive successful go-live outcomes and mentor junior team members\n \n  \n What you’ll need: \n \n 6–10+ years of software engineering experience with a strong backend/systems focus\n Healthcare industry experience, particularly on the payer side (health plans, managed care organizations), including familiarity with healthcare workflows, data standards, and regulatory requirements\n Deep expertise in data architecture and integration patterns: data modeling, transformation logic, schema design, and enterprise integration patterns (middleware, message queues, event-driven architectures)\n Hands-on experience with AWS cloud platforms (Lambda, ECS, RDS, S3, API Gateway, EventBridge)\n Strong API integration and authentication experience: RESTful APIs, webhooks, OAuth2/OIDC, SAML 2.0, JWT, mTLS, and API key management\n Production coding skills in modern languages (Python, Node.js, Java, Go, or similar)\n Experience working directly with enterprise customers in technical implementation roles (not just internal teams)\n Comfort with ambiguity and high agency: ability to define problems, propose solutions, and execute independently\n Direct experience with payer-side technology: prior authorization platforms, utilization management systems, or claims processing is a plus\n Familiarity with healthcare data standards: FHIR, X12 (837, 278, 275), HL7v2, or similar specifications is a plus\n AI/ML integration experience: working with LLMs, embedding AI capabilities into production systems, or ML model deployment is a plus\n Infrastructure-as-code experience with Terraform, CloudFormation, or similar tools is a plus\n Experience in regulated environments (HIPAA, SOC2, HITRUST) is a plus\n Database expertise: SQL and NoSQL (PostgreSQL, MongoDB, DynamoDB), query optimization, and data modeling is a plus\n Identity and access management (IAM) experience: SSO implementations, federated identity, RBAC, and multi-tenancy security patterns is a plus\n \n  \n Pay \u0026 Perks: \n 💻 Fully remote opportunity with about 5% travel\n 🩺 Medical, dental, vision, life, disability insurance, and Employee Assistance Program \n 📈 401K retirement plan with company match; flexible spending and health savings account \n 🏝️ Flex Time Off + company holidays\n 👶 Up to 14 weeks of paid parental leave \n 🐶 Pet insurance  \n The salary range for this position is $128,000 to $145,000 annually; as part of a total benefits package which includes health insurance, 401k and bonus. In accordance with state applicable laws, Cohere is required to provide a reasonable estimate of the compensation range for this role. Individual pay decisions are ultimately based on a number of factors, including but not limited to qualifications for the role, experience level, skillset, and internal alignment.\n  \n Interview Process*: \n \n Connect with Talent Acquisition for a Preliminary Phone Screening\n Meet your Hiring Manager!\n Behavioral Interview(s)\n Case Study\n \n *Subject to change\n  \n About Cohere Health: \n Cohere Health’s clinical intelligence platform delivers AI-powered solutions that streamline access to quality care by improving payer-provid","salary_min":128000,"salary_max":145000,"location":"United States","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["api-design","healthcare","cloud","llm","payments","mlops"],"apply_url":"https://job-boards.greenhouse.io/coherehealth/jobs/7704116003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T14:33:54Z","expires_at":"2026-05-19T14:07:45.136924Z","created_at":"2026-04-17T19:34:13.379323Z","updated_at":"2026-04-19T14:07:45.211049Z","company_name":"Cohere Health","company_slug":"cohere-health","company_logo_url":"https://www.google.com/s2/favicons?domain=coherehealth.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bd857cce-b68e-4773-a809-8d7611bf3956"}],"page":1,"per_page":20,"total":3226,"total_pages":162}