{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL (Reinforcement Learning) ","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","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["gpu","alignment","search","jax","distributed-systems","code-generation","pytorch","llm"],"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":"2026-08-14T14:00:28.788703Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-07-15T14:00:28.927351Z","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":"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","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","llm","alignment","reinforcement-learning","pre-training","search","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":"2026-08-14T14:00:30.363031Z","created_at":"2026-04-13T09:36:01.625992Z","updated_at":"2026-07-15T14:00:30.486844Z","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":"8e714354-f0cc-4558-b706-5f155771b9bb","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Technical Lead Manager, Machine Learning Runtime \u0026 Serving","slug":"technical-lead-manager-machine-learning-runtime-serving-3bc85bbd","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 Waymo is seeking a senior Technical Lead Manager (TLM) Machine Learning Engineer to guide the technical vision of our core ML infrastructure. In this role, you will actively grow and manage a high-performing team of 6 engineers to deliver Waymo’s next-generation ML ecosystem. This critical work encompasses both the in-vehicle inference engine and the cloud-based serving infrastructure for our foundational models. You will architect scalable, high-performance ML runtime systems that operate across two extreme domains: the highly constrained edge compute environment of autonomous vehicles and our large-scale, offboard data centers.\n You will: \n \n Guide the technical vision of our core ML infrastructure while actively growing and managing a high-performing team of 6 engineers to deliver Waymo’s next-generation ML ecosystem, encompassing both the in-vehicle inference engine and the cloud-based serving infrastructure for our foundational models.\n Architect scalable, high-performance ML runtime systems that operate flawlessly across two extreme domains: the highly constrained edge compute environment of autonomous vehicles and our large-scale, offboard data centers.\n Navigate complex engineering trade-offs, driving feature development that seamlessly balances the strict, real-time latency and memory limits of onboard execution with the high-throughput, highly concurrent demands of fleet-scale cloud serving.\n Spearhead the strategic transition of core ML workloads to a JAX-native runtime architecture, which includes actively extending and modifying underlying ML compilers and runtimes (e.g., OpenXLA/PjRT, TensorRT).\n Partner across organizational boundaries with world-class ML researchers in Perception and Planning to deeply analyze system-level workloads and unlock massive performance gains through hardware-aware compute optimizations.\n Drive systemic performance excellence by designing advanced profiling and benchmarking infrastructure to identify, triage, and eliminate bottlenecks across the entire end-to-end ML software stack.\n \n You have: \n \n B.S. or M.S. in CS, EE, Deep Learning or a related field.\n People management experience, with a proven track record of recruiting, mentoring, and guiding high-performing teams of senior engineers.\n 8+ years of professional software engineering experience architecting, building, and scaling complex ML systems and infrastructure.\n Strong production programming expertise.\n Proven track record of optimizing ML software to maximize the performance of hardware accelerators (e.g., GPUs, TPUs, or custom silicon).\n Hands-on experience developing distributed backend systems that are low-latency, highly concurrent, and fault-tolerant at scale.\n \n We prefer:  \n \n PhD in CS, EE, Deep Learning or a related field.\n Deep expertise in modifying and extending ML software stacks, including compilers, runtimes, or inference engines (e.g., OpenXLA/PjRT, TensorRT, ONNX Runtime, TVM).\n Strong background in building and scaling LLM serving systems, leveraging advanced distributed inference and performance optimization techniques.\n Deep expertise in edge computing and automotive ML deployment, navigating strict power, thermal, and real-time latency constraints to optimize and deploy mission-critical models on resource-constrained embedded hardware.\n \n \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 $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","autonomous-vehicles","llm","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8062303","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T01:09:13Z","expires_at":"2026-08-14T14:06:32.954628Z","created_at":"2026-07-15T14:06:33.079101Z","updated_at":"2026-07-15T14:06:33.079101Z","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/8e714354-f0cc-4558-b706-5f155771b9bb"},{"id":"7466ea68-e22a-4989-93a5-1db0ae5979e1","company_id":"a0000000-0000-0000-0000-000000000003","title":"Software Engineering Manager, Public Sector ","slug":"software-engineering-manager-public-sector-dd16fefc","description":"Scale AI’s Public Sector business is growing quickly as government agencies adopt AI to support critical national security, defense, and public sector missions. We’re looking for a hands-on Engineering Manager to lead a team of software engineers building core products and infrastructure for these customers.\n This role is ideal for someone who thrives in technical environments, enjoys managing teams while staying close to the code, and wants to work on meaningful problems that impact real world operations across the U.S. government. You’ll play a critical role in delivering backend systems, distributed platforms, and ML tooling used by our public sector partners—all while helping your team grow and execute.\n You’ll split your time between technical planning and execution (50%) and people management and team development (50%) , leading a team of 6-8 engineers. You’ll work cross-functionally with product, security, and customer-facing teams to ensure our engineering efforts meet complex federal compliance, security, and performance needs.\n Must be able to commute to office three times per week \n You will: \n \n Recruit a high-performing engineering team. \n Drive engineering productivity. Provide guidance, mentorship, and technical leadership to a team of engineers working on Generative AI projects. \n Collaborating with cross-functional teams to define, design, and execute strategic roadmap.\n Navigate and deliver outcomes while navigating through complex public sector compliance requirements and frameworks.\n Design and implement scalable backend systems for Federal customers, leveraging Scale's modern and cloud-native AI infrastructure\n Develop distributed systems, data-intensive applications, and machine learning infrastructure to enable real impact for mission owners\n Build robust and reliable backend systems that can serve as standalone products, empowering customers to accelerate their own AI ambitions\n Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions\n Contribute to the platform roadmap and product strategy for Scale AI's Federal business, playing a key role in shaping the future direction of our offerings\n Have or ability to obtain a TS/SCI clearance \n \n Ideally you’d have: \n \n 5+ years of full-time engineering experience, post-graduation\n 2+ years of prior engineering management or equivalent experience and has managed an engineering team.\n Have extensive experience in software development\n Experience scaling products at hyper-growth startups\n Excitement to work with AI technologies and their applications for the public sector\n Extremely strong track record as an individual contributor\n Show a track record of mentoring and leading teams in successful projects\n Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders\n \n Nice to haves: \n \n TS/SCI Clearance\n Deep technical knowledge of Software Development, willing to get deep into the weeds to solve problems alongside the team.\n Have experience with AI platforms and technologies, including generative models and LLMs.\n Have previous experience in government or government facing technology roles\n Experience with cloud-native technologies, full stack development, data engineering, and ml ops infrastructure\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 and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, 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 is:\n $216,000 — $270,000 USD \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 Washington DC is:\n $194,400 — $243,000 USD \n Please reference the job posting's subtitle for wher","salary_min":162400,"salary_max":203000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","distributed-systems","generative-ai","mlops"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4715325005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T01:05:55Z","expires_at":"2026-08-14T14:01:49.051439Z","created_at":"2026-07-15T14:01:49.182522Z","updated_at":"2026-07-15T14:01:49.182522Z","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/7466ea68-e22a-4989-93a5-1db0ae5979e1"},{"id":"be679834-9c8e-4780-bdad-f2d02b24a22e","company_id":"a0000000-0000-0000-0000-000000000001","title":"Safeguards Enforcement Analyst, Violence \u0026 Extremism","slug":"safeguards-enforcement-analyst-violence-extremism-5a4dffe7","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 Safeguards Enforcement Analyst focused on Violence \u0026 Extremism, you will be responsible for building and executing operational workflows to assess model behavior, drive enforcement decisions, and develop evals across a technically demanding range of policy areas. Your work spans detecting and mitigating attempts to misuse Anthropic's AI systems to facilitate real-world harm, including weapons and dangerous technology, critical infrastructure attacks, violent extremism, and threats of violence.\n Important context for this role: In this position you may be exposed to and engage with explicit content spanning a range of topics, including those of a violent, graphic, hateful, or psychologically disturbing nature.\n Key responsibilities \n \n Design and architect automated enforcement systems and review workflows that scale effectively while maintaining high accuracy\n Develop and maintain evals that measure model performance on these policy areas, surface regressions, and inform policy and model improvements\n Partner with Engineering and Data Science to optimize detection and automated enforcement systems for potential policy violations\n Review flagged content to drive enforcement decisions and surface policy gaps, with particular attention to novel or technically sophisticated misuse attempts + emerging extremist movements, ideologies, and mobilization tactics\n Support the Safeguards policy design team by providing structured feedback on policy gaps and enforcement ambiguities based on real enforcement scenarios\n Develop and maintain enforcement guidelines and reviewer documentation that enable accurate, consistent enforcement across a wide range of content\n Keep up to date with emerging threats, terrorist and extremist movements, regulatory changes, and AI policy enforcement best practices, and apply these to inform our workflows and evals\n Identify and escalate emerging misuse patterns, novel attack vectors, and signs of coordinated violent extremist activity\n \n Minimum qualifications \n \n Experience in policy enforcement, threat intelligence, counterterrorism, government, or a closely related field, with direct exposure to harmful content, dangerous technology, violent extremism, or physical harm facilitation\n Experience standing up and scaling policy enforcement or content review workflows\n Proficiency in SQL and/or other data analysis tools to draw insights from large datasets and monitor enforcement workflow health\n Experience identifying emerging risks and threat actors, and communicating findings to a diverse set of stakeholders, such as Product, Policy, Engineering, and Legal teams\n Experience working with generative AI products, including writing effective prompts for content review and enforcement\n Understanding of the challenges involved in implementing product policies at scale, including in the content moderation space\n \n Preferred qualifications \n \n Subject matter expertise in one or more high-stakes harm areas, such as weapons and dangerous technology, violent extremism, terrorism, autonomous systems, or critical infrastructure protection\n Familiarity with relevant legal and regulatory frameworks governing dangerous technology, critical infrastructure, or domestic/international terrorism\n Experience developing evals or red-teaming AI systems, particularly for harmful content or policy enforcement use cases\n Experience with threat actor profiling and threat intelligence frameworks (e.g., MITRE ATT\u0026CK)\n Experience tracking threat actors, extremist networks, or misuse patterns across surface, deep, and dark web environments\n Experience with large language models and an understanding of how AI technology could provide meaningful uplift toward serious harm\n Proficiency in Python for data analysis and workflow automation\n Background in law enforcement, national security, defense, counterterrorism, or a relevant regulatory environment\n Experience assessing the technical plausibility and real-world harm potential of content, including the ability to distinguish between general educational content and genuine operational uplift, and between protected speech and genuine incitement/mobilization\n Familiarity with cross-platform threat analysis and OSINT techniques\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 $285,000 — $330,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of ","salary_min":285000,"salary_max":330000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","generative-ai","alignment","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5343907008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T00:47:43Z","expires_at":"2026-08-14T14:00:32.214041Z","created_at":"2026-07-15T14:00:32.344861Z","updated_at":"2026-07-15T14:00:32.344861Z","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/be679834-9c8e-4780-bdad-f2d02b24a22e"},{"id":"d5817836-ec6a-4a44-8482-6cb7a1c60532","company_id":"ab3e4567-6f87-4ccf-9ec0-81fd82105f48","title":"Senior Data Scientist, Detection","slug":"senior-data-scientist-detection-6c1be6df","description":"About Us \n \n At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. \n At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a \"normalized\" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in.\n Available Locations- New York\n About the Role \n Cloudflare’s Engineering Team is home to some of the industry’s top engineers, dedicated to building and scaling innovative software that handles a huge proportion of the Internet. Our Detection department sits at the heart of that mission: we identify automated, fraudulent, and malicious activity across the Internet and through our gateway. We develop advanced detection systems and machine learning models that operate at scale, collaborating with Product and Engineering teams across the company to protect our customers and stay ahead of the constantly evolving threat landscape.\n Responsibilities \n \n Research, design, and evaluate detection models that identify automated, fraudulent, and malicious activity across Internet-scale data.\n Dig into massive datasets to uncover the patterns and behaviors that distinguish adversaries from legitimate users.\n Define how detection success is measured, designing metrics and evaluation strategies for problems where ground truth is noisy, delayed, or contested.\n Stay current on emerging AI/ML research and evaluate how new techniques (e.g., LLMs, generative AI) can be applied to our products.\n Partner with ML Engineers, Data Engineers, and Product to take detection approaches from research to production and measure their real-world impact.\n \n Desirable Skills, Knowledge, and Experience  \n \n Fraud and bots at scale. You have experience across fraud, abuse, and/or bot detection on large, high-velocity traffic. You may focus on one, but you transfer instincts between them.\n Strong fundamentals, fluent in data. You have solid applied statistics, machine learning, and AI methodology fundamentals. You choose the right technique for the problem, and are fluent with large-scale data.\n You have at least 5-7 years of experience professionally working in Data Science, ML Engineering, or Software Engineering. \n You are very comfortable with Python \u0026 SQL in production environments.\n \n Bonus points \n \n At home in ground truth ambiguity. Building detections when ground truth is scarce is the heart of this job. You make real progress with weak, delayed, or absent labels and you're energized by adversaries that fight back.\n You don't burn signals. You understand (or are curious to learn) how to act on detections without tipping your hand, knowing that how you deploy and respond can erode your future visibility.\n Pragmatic about complexity. You know when a simple solution beats a complex one, and you don't chase small gains at disproportionate cost.\n Disciplined in code. You apply strong programming and engineering best practices in both research and production code.\n Impact-driven and clear. You connect your work to business impact and communicate clearly across technical and non-technical stakeholders.\n \n Compensation \n Compensation may be adjusted depending on work location.\n \n  For New York City based hires: Estimated annual salary of $185,000 - $231,000.\n \n Equity \n This role is eligible to participate in Cloudflare’s equity plan.\n Benefits \n Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employ","salary_min":185000,"salary_max":231000,"location":"Hybrid","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","llm","generative-ai","data-science"],"apply_url":"https://boards.greenhouse.io/cloudflare/jobs/8042547?gh_jid=8042547","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T00:45:56Z","expires_at":"2026-08-14T14:11:16.253949Z","created_at":"2026-07-15T14:11:16.38206Z","updated_at":"2026-07-15T14:11:16.38206Z","company_name":"Cloudflare","company_slug":"cloudflare","company_logo_url":"https://www.google.com/s2/favicons?domain=cloudflare.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d5817836-ec6a-4a44-8482-6cb7a1c60532"},{"id":"9cf703e4-28cb-47a7-9151-d26f9745f43d","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Machine Learning Engineer, Relevance and Personalization (Query Intelligence)","slug":"senior-machine-learning-engineer-relevance-and-personalization-query-intelligence-b6fdeb9a","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n The Community You Will Join: \n The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. In this role you'll focus on query intelligence, the front door of search working on critical, impactful projects that turn what a guest types, taps, or says into a precise understanding of their intent, spanning autocomplete and smart compose, query tagging, query expansion, and intent modeling across Stays, Experiences, and Services.\n The Difference You Will Make: \n Query understanding is where every search begins, and it directly shapes retrieval, ranking, and ultimately the perfect match between guests and hosts. We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) and increasingly large language models at Airbnb. You'll build the models that parse free-form and natural-language multimodal queries, extract entities and location context, classify intent, and anticipate what guests want before they finish typing. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb's mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here: https://sites.google.com/view/airbnb-relevance-publications/home \n A Typical Day:  \n \n Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases, with a focus on query understanding.\n Develop query understanding capabilities — autocomplete and smart compose, query tagging (sequence tagging / NER), query expansion, and query/user intent modeling — and natural-language (\"search in your own words\") search experiences powered by modern NLP and LLMs.\n Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.\n Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.\n Leverage third-party and in-house Machine Learning tools \u0026 infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.\n Example projects include: smart compose and language generation for search, LLM-based sequence taggers, LLM-driven query/location expansion, intent classification, and user-intent sequence modeling.\n \n Your Expertise: \n \n 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.\n Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.\n Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. natural language processing, personalization, search and recommendation, marketplace optimization).\n Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).\n Industry experience building end-to-end Machine Learning models.\n Experience applying large language models and modern NLP — e.g., sequence tagging/NER, text generation, intent classification, or embedding/representation learning.\n Familiarity with building natural-language, AI-native and agentic search experiences is a plus.\n Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your po","salary_min":200000,"salary_max":235000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","generative-ai","data-pipeline","agents","search","nlp","pytorch","tensorflow"],"apply_url":"https://careers.airbnb.com/positions/8065789?gh_jid=8065789","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T23:54:51Z","expires_at":"2026-08-14T14:11:22.875202Z","created_at":"2026-07-15T14:11:23.002744Z","updated_at":"2026-07-15T14:11:23.002744Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9cf703e4-28cb-47a7-9151-d26f9745f43d"},{"id":"021f3b70-f0d5-4666-a5e1-431d120b0e63","company_id":"31ae48bc-c938-4c26-a348-0bf3c089a446","title":"Senior Software Engineer - GPU Kernel Authoring \u0026 Optimization","slug":"senior-software-engineer-gpu-kernel-authoring-optimization-d4eed12b","description":"CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com . \n About the role: \n CoreWeave is the top-rated AI-cloud for high-performance GPU infrastructure across AI/ML, visual effects, rendering, and real-time inference. Our stack is engineered for speed, scale, and cost-efficiency—an unmatched alternative to traditional hyperscalers. At CoreWeave, infrastructure is the product.\n We're looking for a Senior Engineer for CoreWeave's Benchmarking \u0026 Performance team, focused on kernel authoring and optimization. You will write, profile, and tune the GPU kernels that sit on the critical path of large-scale model serving—squeezing maximum throughput and minimum latency out of every SM, tensor core, and byte of memory bandwidth. You will also aid us in achieving industry-leading end-to-end performance benchmarking publications such as MLPerf.\n You will be an owner who leads designs, raises engineering standards, and delivers measurable improvements to latency, throughput, and reliability across our inference stack. You'll partner with product, orchestration, and hardware teams to turn kernel-level wins into end-to-end gains and meet strict P99 SLAs at scale.\n \n Author, profile, and optimize CUDA kernels—GEMMs, attention, MoE routing, quantization, KV-cache, and fused epilogues—on the critical path of LLM inference.\n Optimize for the hardware: exploit tensor cores and tune occupancy, memory coalescing, shared-memory/register usage, and overlap of compute with data movement.\n Use kernel-authoring DSLs and compilers to prototype and ship kernels quickly without sacrificing performance.\n Benchmark rigorously: build reproducible microbenchmarks and roofline analyses, and validate that kernel-level wins translate to end-to-end latency/throughput gains across model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang).\n Implement and maintain benchmarking workflows for end-to-end MLPerf Inference (and Training) runs, including workload setup, cluster configuration, runbooks, and result validation.\n Lead design reviews and drive architecture within the team; decompose multi-service work into clear milestones.\n Mentor junior engineers; review cross-team designs and elevate coding/testing standards.\n Help ensure reproducible, well-documented benchmarking and kernel-optimization processes.\n \n Who You Are: \n \n 5+ years of experience building high-performance computing, GPU/accelerator software, or performance-critical systems.\n Hands-on CUDA experience is required—you have written and optimized custom kernels and are fluent with the CUDA programming and memory model.\n Deep understanding of GPU architecture and performance: tensor cores, warp/occupancy tuning, the memory hierarchy and bandwidth, NVLink/PCIe, and profiling with Nsight Compute/Systems.\n Strong coding in C++ and Python; comfortable reading and writing low-level, performance-sensitive code.\n Familiarity with model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang) and the kernels that dominate their inference cost.\n Strong communicator comfortable collaborating with cross-functional teams and external partners.\n \n Preferred: \n \n Triton or Mojo for authoring custom GPU kernels — highly desired.\n CuTe DSL for Python-based kernel authoring on NVIDIA GPUs.\n JAX and its Pallas kernel language for authoring kernels on GPU/TPU.\n HIP / ROCm and AMD GPU experience.\n NCCL and collective-communication performance.\n Experience with alternative accelerators such as Google TPUs and Meta's MTIA.\n Familiarity with kernel-authoring DSLs and nano-compilers such as KNYFE and its Block DSL.\n Experience with Kubernetes at production scale.\n Experience with SUNK (Slurm on Kubernetes) / Slurm for scheduling large GPU jobs.\n Experience running MLPerf submissions or similar large-scale audited benchmarks.\n Contributions to OSS projects such as vLLM, SGLang, PyTorch, Triton, or CUTLASS.\n \n Wondering if you're a good fit? \n We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match.\n Why CoreWeave? \n Help shape an industry-defining inference platform that enables teams to deploy generative AI and real-time applications at scale. If squeezing every last microsecond out of GPU kernels and delivering reliable model serving excites you, this is the place to build. We're in an exciting stage of hyper-growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly ","salary_min":182000,"salary_max":242000,"location":"Sunnyvale, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","generative-ai","llm","pytorch","computer-graphics","gpu","jax"],"apply_url":"https://coreweave.com/careers/job?4697100006\u0026board=coreweave\u0026gh_jid=4697100006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T22:01:55Z","expires_at":"2026-08-14T14:06:51.780451Z","created_at":"2026-07-15T14:06:51.909822Z","updated_at":"2026-07-15T14:06:51.909822Z","company_name":"CoreWeave","company_slug":"coreweave","company_logo_url":"https://www.google.com/s2/favicons?domain=coreweave.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/021f3b70-f0d5-4666-a5e1-431d120b0e63"},{"id":"66be6f1d-738c-4b9b-b07d-4cae69e7b29d","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Machine Learning Engineer, Agent Oversight","slug":"senior-machine-learning-engineer-agent-oversight-774633fc","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Machine Learning Engineer on Agent Oversight, you will drive the end-to-end lifecycle that ensures our production agents perform reliably and improve over time. This includes building observability tools, designing robust evaluation frameworks, and developing improvement loops. Whether scaling infrastructure or researching new improvement methods, you will navigate the entire ML loop while maintaining rigorous technical standards.\n You will:\n \n Build or contribute to observability into agent behavior in production — the signals and instrumentation needed to actually see what an agent is doing, not just whether it succeeded or failed\n Design evaluation methodologies and metrics for agentic applications, and work with the platform to make them run automatically, at scale, across different customer use cases, not just as one-off analyses\n Build, ship, and own ML systems that detect drift, anomalies, or misalignment in production agent behavior — from first prototype through running reliably at scale\n Design and run rigorous experiments to validate model and agent performance improvements before they ship\n Work alongside software engineers on the platform where your work intersects with broader infrastructure — but you’re expected to take your own work from idea to production, not hand it off\n Collaborate closely with product managers, customers, data annotators, Forward Deployed Engineers, and other engineering teams to translate enterprise and government requirements into robust platform capabilities\n Depending on focus, contribute to novel methods and approaches that push the state of the art for agent evaluation and improvement, or focus on building ML systems that hold up reliably at scale in production\n \n Requirements:\n \n 5+ years of experience as an ML engineer or applied scientist, ideally on a production ML or LLM-powered system — not just consuming a third-party ML API within a feature\n Strong grounding in  at least two  of the following:\n \n Building or scaling evaluation, monitoring, or continuous-learning infrastructure for ML/agentic systems\n Design experience for agent systems (architecture, orchestration, tool use)\n Developing new methods, reward models, or model training/fine-tuning approaches\n \n Hands-on experience with LLMs and agent architectures — tool use, planning, multi-agent orchestration\n Comfortable partnering with software engineers to productionize research and experimental work, not just deliver a one-off analysis\n Rigorous approach to experimentation: clear hypotheses, real statistical grounding, and results that hold up under scrutiny\n Track record of collaborating across functions (Product, Forward Deployed Engineering, etc.) to navigate ambiguous requirements and bring them to production\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Experience building or contributing to RLHF, SFT, or other fine-tuning/RL workflows, reward modeling, or verifiable-reward systems\n Experience with model or systems optimization (e.g., latency, cost, or inference efficiency)\n Published research, open-source contributions, or patents in agentic systems, LLMs, or applied ML\n Experience working in regulated or enterprise contexts\n Track record of taking a novel method from prototype to something running reliably in production, navigating ambiguity along the way\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\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 and may be inclusive of several career level","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","llm","fine-tuning","agents","reinforcement-learning","autonomous-vehicles","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714527005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:14:32Z","expires_at":"2026-08-14T14:01:47.147912Z","created_at":"2026-07-15T14:01:47.280877Z","updated_at":"2026-07-15T14:01:47.280877Z","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/66be6f1d-738c-4b9b-b07d-4cae69e7b29d"},{"id":"96c4b57f-c214-4de0-829c-cda4957c7a17","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Software Engineer, Agent Oversight","slug":"senior-software-engineer-agent-oversight-a8682235","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power Agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Software Engineer on Agent Oversight, you will build the platform infrastructure that lets our production agents be observed, evaluated, and improved at scale. This includes building observability tooling, evaluation harnesses, and the pipelines that connect them to improvement loops. Whether building foundational infrastructure or partnering closely with ML engineers on production workflows, you will own your systems end-to-end while maintaining rigorous technical standards.\n You will:\n \n Design and build core platform capabilities for deploying, monitoring, and evaluating agentic applications in production\n Build reliable APIs and data pipelines that capture agent telemetry, evaluation signals, and performance metrics at scale\n Work alongside ML engineers where platform work intersects with evaluation or improvement systems — bringing enough ML fluency to reason about model behavior, evaluation quality, and improvement loops while owning the software systems that make those workflows reliable\n Own the reliability, scalability, and observability of platform components serving multiple concurrent enterprise and government customers\n Work cross-functionally with product, forward deployed engineering, and customers to translate real-world deployment requirements into platform features\n Build features end-to-end: system design, implementation, debugging, and testing\n Participate in high-velocity experimentation to validate platform capabilities against real customer usage\n \n Requirements:\n \n 4+ years of professional software engineering experience, with strong fundamentals in backend/distributed systems, APIs, and data pipeline design\n Hands-on experience building production software for ML/LLM-powered products or platforms, such as evaluation systems, observability/monitoring, experimentation infrastructure, agent runtimes, model-serving-adjacent services, or telemetry/data pipelines\n Working knowledge of how LLM or ML systems behave in production: evaluation signals, failure modes, prompt/tool-calling workflows, experiment results, data quality issues, and the tradeoffs between offline evals and live customer behavior\n Experience partnering closely with ML engineers or applied researchers to turn prototypes, eval loops, or model-improvement workflows into reliable platform capabilities, without needing to own model training, modeling strategy, or research direction\n Experience building infrastructure or platforms that other engineering teams build on top of (internal platform, developer tools, or similar)\n Track record of taking ownership of features or components end-to-end — from design through production — within a larger platform or system\n Comfortable operating in an ambiguous, fast-changing domain where tooling and best practices are still being defined\n Strong problem-solving skills and the ability to work independently or as part of a tight-knit, cross-functional team\n Excited to work directly with ML engineers and customer-facing teams, including challenging assumptions in designs and metrics when platform behavior, model behavior, and customer needs intersect\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Deep experience building or maintaining observability, monitoring, or evaluation systems for ML/LLM-powered products in production\n Familiarity with agent architectures — tool use, planning, multi-agent orchestration\n Exposure to MLOps, feature stores, model serving, or experiment infrastructure\n Experience working in regulated or enterprise contexts\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\n Compensation packages at Scale for eligible roles include base s","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","mlops","generative-ai","agents","autonomous-vehicles","llm","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714509005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:12:46Z","expires_at":"2026-08-14T14:01:47.306812Z","created_at":"2026-07-15T14:01:47.543291Z","updated_at":"2026-07-15T14:01:47.543291Z","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/96c4b57f-c214-4de0-829c-cda4957c7a17"},{"id":"1a206bd4-e5b5-4a4d-8384-65e3e9c3f4ec","company_id":"2721f049-2cf2-4e3e-82d0-8d8df89c8f90","title":"SDR, Tavily","slug":"sdr-tavily-497ebf81","description":"About Nebius: \n Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.\n Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\n Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R\u0026D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R\u0026D.\n \n About Tavily \n We’re building the search engine for AI agents. Our API is designed from the ground up to power RAG and real-time reasoning in AI systems. By connecting LLMs to high quality, trustworthy web content, we help developers build agents that are not only intelligent, but also informed.\n We work with some of the most innovative teams in AI, from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it’s powering sales assistants, research copilots, or internal knowledge tools, we’re the missing link between LLMs and the real world \n The role \n We’re hiring to expand on the immediate success and impact our founding SDR team has had. You will be the engine behind the engine, helping convert high-volume developer inbound into qualified opportunities and building an outbound motion to the teams creating cutting edge agents and AI products.\n Your responsibilities will include:   \n \n Promptly follow up with inbound and outbound prospects via email, LinkedIn, and calls to ensure no lead slips through the cracks.\n Build a deep understanding of Tavily’s ICP: AI engineers, data science teams, and product leaders building agentic systems to identify where they need grounded, real-time search in their product\n Qualify opportunities and book meetings for the Sales team, ensuring they are equiped with the correct information to win the deal.\n Provide structured feedback on signals, workflows, and outputs to help us improve Tavily based on real-life testing.\n \n We expect you to have:   \n \n 1–2 years of Sales, SDR, Analytics or Computer Science experience in a SaaS or tech environment preferred (open to exceptional entry-level candidates).\n You are in Austin and excited about an in-person office environment (think 4 days per week).\n You're resilient, energized by building relationships, and genuinely excited to learn.\n You’re genuinely curious about AI and enjoy learning what new products and teams are building, even if you’re not technical yourself.\n Ability to balance high-volume outreach with thoughtful experimentation and feedback.\n \n Key employee benefits in the US: \n \n Health insurance:  100% company-paid medical, dental, and vision coverage for employees and families.\n 401(k) plan:  Up to 4% company match with immediate vesting.\n Parental leave:  20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.\n Remote work reimbursement:  Up to $85/month for mobile and internet.\n Disability \u0026 life insurance : Company-paid short-term, long-term and life insurance coverage.\n \n \n Pay Transparency \n We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.\n Base Compensation Range\n $71,700 — $89,600 USD \n Benefits \u0026 Perks: \n \n Competitive compensation\n Career growth and learning opportunities\n Flexibility and ownership\n Collaborative and innovative culture\n Opportunity to work on impactful AI projects\n International environment and talented teams\n \n What's it like to work at Nebius: \n Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI \n Equal Opportunity Statement: \n Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.\n Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. \n If you need accommodations during the application process, please let us know.","salary_min":71700,"salary_max":89600,"location":"Austin, TX","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["search","cloud","code-generation","rag","llm","agents"],"apply_url":"https://careers.nebius.com/?gh_jid=4927819101","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:05:03Z","expires_at":"2026-08-14T14:17:14.484341Z","created_at":"2026-07-15T14:17:14.584259Z","updated_at":"2026-07-15T14:17:14.584259Z","company_name":"Nebius","company_slug":"nebius","company_logo_url":"https://www.google.com/s2/favicons?domain=nebius.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1a206bd4-e5b5-4a4d-8384-65e3e9c3f4ec"},{"id":"0003f63a-b2b2-44e0-b588-7a3de39a2516","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"Agent Experience Designer, Agentic Voice","slug":"agent-experience-designer-agentic-voice-00a4cb3f","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 Agent Experience Designer — Agentic Voice, you’ll own the voices, personalities, and interactions that make an AI agent feel intuitive, empathetic, and human. We are going all-in on agentic AI under one core idea: stop answering, start resolving. A voice agent that resolves is only as good as the experience it delivers, and designing that entire voice experience is your job. \n Reporting directly to the VP of AI Products, you’ll collaborate hand-in-hand with our AI engineers to shape model judgment through prompts and flow orchestration, rather than hard-coded branches. You’ll also help create a centralized persona system, voice standards, and the universal quality bar that forward-deployed VX designers will apply account-by-account in the field. \n In addition, you’ll help bring a deep sense of behavioral and emotional design to our platform, ensuring our agents have the taste, pacing, and vocabulary to sound truly competent and empathetic across both happy paths and high-stakes moments. \n This position has the opportunity to be based in our San Ramon, US office.\n What you’ll do \n \n Own the agent's global voice, character, and personality, maintaining personal consistency across every vertical we ship. \n Own the standard handoff patterns and design systems, ensuring seamless transitions where context is fully preserved when an agent passes a caller to a human. \n Own the universal platform quality bar, defining and measuring Consistency, Fluency, and Latency (CFL) and tying personal decisions directly to core metrics like resolution, containment, and sentiment. \n Make the voice palette and establish house standards for pacing, prosody, and emphasis that forward-deployed teams will use to build brand-specific experiences. \n Partner with AI engineers to orchestrate behavior, escalation instincts, confirmation patterns, and graceful recovery workflows using advanced prompting rather than rigid dialogue trees. \n Research and design for distinct behavioral and emotional user states, ensuring the agent adapts seamlessly whether interacting with a patient disputing a bill or a dispatcher tracing a late delivery. \n \n Skills you’ll bring \n \n Experience: 5+ years of dedicated experience shaping voice user interfaces (VUI), character writing, conversation design, or complex conversational/agentic systems. \n Bachelor's degree in Linguistics, Communication, Psychology, Design, or equivalent practical experience. \n Demonstrated experience shaping voice user interfaces (VUI), character writing, or complex conversational/agentic systems. \n Fluency with LLM-based agent behaviors, prompt engineering, and prompt orchestration (knowing how design choices alter model outputs without relying on code). \n Fluency with Text-to-Speech (TTS) controls, including voice selection, SSML tuning, pacing, and emphasis to set broad platform standards. \n An exceptional portfolio that highlights voice systems, written persona standards, and interactive logic rather than just static flow diagrams. \n Experience in regulated, high-stakes verticals (e.g., healthcare, financial services, legal) is a strong plus. \n Strong taste and an ear for dialogue—the ability to articulate a character on a page and translate it into consistent AI behavior under pressure. \n \n For exceptional talent based in California, 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","salary_min":147000,"salary_max":186000,"location":"San Ramon, US","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","speech","llm","healthcare"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8633475002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:04:06Z","expires_at":"2026-08-14T14:23:00.471126Z","created_at":"2026-07-15T14:23:00.567276Z","updated_at":"2026-07-15T14:23:00.567276Z","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/0003f63a-b2b2-44e0-b588-7a3de39a2516"},{"id":"d3e4d203-9a98-43d2-b9d4-af63039179a3","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Machine Learning Engineer, Sensor Pipelines","slug":"machine-learning-engineer-sensor-pipelines-d82bbef2","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 Perception team at Waymo builds technology that powers the Waymo Driver. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We conduct research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling software engineers like you to develop multi-modal models and techniques at scale.\n The Sensor Pipelines team applies sensor fusion and ML approaches to address critical challenges in Perception; like detections of Collisions, Antagonistic Behaviors like Vandalism, Sensing Occlusions, etc. Our work involves cutting-edge research (Gen AI) to solve real-world problems and requires close collaboration with onboard teams across Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to develop sophisticated models and techniques at scale.\n This role follows a hybrid work schedule and reports to a Technical Lead Manager.\n You will: \n \n Apply sensor fusion, machine learning techniques to build multi-modal sensor fusion architectures and spatial-temporal representation learners to solve real-world challenges\n Develop and deploy machine learning models, including using Generative Artificial Intelligence (Gen AI) system, and non-ML systems to solve those challenging problems\n Develop data mining, labeling, training and eval pipelines to support the onboard development\n Collaborate and work in partnership with product, infra and research teams across Waymo\n \n You have: \n \n Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience\n 3+ years experience in Machine Learning and/or Computer Vision\n Experience with C++ and Python\n Experience with ML frameworks like PyTorch or JAX\n \n We prefer: \n \n MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline\n Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI\n Github repositories or Tech Blogs of LLMs/ VLMs\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 $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["pytorch","autonomous-vehicles","computer-vision","llm","deep-learning","generative-ai","robotics","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8051390","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:03:11Z","expires_at":"2026-08-14T14:06:24.797122Z","created_at":"2026-07-15T14:06:24.923116Z","updated_at":"2026-07-15T14:06:24.923116Z","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/d3e4d203-9a98-43d2-b9d4-af63039179a3"},{"id":"dbf3ed2d-61a8-46d4-8f99-13cd04d28f0e","company_id":"4c109027-78ec-41cd-b57a-dc58e47d0bd0","title":"Senior Machine Learning Scientist I, Model-Driven Optimization","slug":"senior-machine-learning-scientist-i-model-driven-optimization-eb80e6bd","description":"The Role:  \n Generate:Biomedicines is seeking a creative, rigorous, and execution-oriented machine learning scientist to join our Model-Driven Design team. This role will focus on building the ML methods, data strategies, and closed-loop systems that determine what we design, build, test, and learn from next.\n The Model-Driven Design team works at the interface of machine learning, protein design, engineering, and experimental science. We develop and apply models and quantitative frameworks that help Generate discover and optimize therapeutic proteins. In this role, you will help advance the technical foundation of our lab-in-the-loop protein optimization platform, with a focus on sequential decision-making, experimental design, property modeling, and scalable design systems.\n We are looking for someone who can serve as a technical leader and hands-on individual contributor, driving complex, high-impact work from problem framing through implementation, deployment, and experimental impact. The ideal candidate combines depth in probabilistic machine learning, Bayesian optimization, active learning, or related approaches with the practical judgment and engineering discipline to turn technical ideas into reliable systems that drive impact. You will partner closely with protein designers, wet-lab scientists, ML scientists, and engineers to build durable capabilities that accelerate therapeutic discovery.\n This role is part of a highly collaborative team environment that balances in-person collaboration with hybrid flexibility based out of our Somerville, MA office. \n Here's how you will contribute: \n \n Develop new machine learning methods and systems for lab-in-the-loop protein optimization, including property models and multi-objective optimization strategies for therapeutic protein design.\n Shape data-generation and data-use strategies that make experimental campaigns maximally informative for model improvement, therapeutic optimization, and future design cycles.\n Build and apply LLM-enabled and agentic workflows that help scientists explore design hypotheses, connect models to data and experiments, and accelerate iterative learning.\n Design, implement, test, and maintain production-quality ML models, software components, and data workflows, with attention to reliability, reproducibility, observability, and computational efficiency.\n Partner with ML engineering and software teams to integrate these components into robust, scalable platform capabilities, with clear ownership across team boundaries.\n Collaborate closely with protein designers and wet-lab scientists to ensure models and optimization systems are grounded in experimental reality and deliver measurable impact.\n Identify important technical gaps, develop proposals, define milestones, align stakeholders, and help set technical direction across cross-functional programs.\n Communicate clearly across disciplines and help raise technical standards across ML, engineering, protein design, and experimental teams.\n \n The Ideal Candidate will have: \n \n PhD in machine learning, computational biology, computer science, applied mathematics, engineering, or a related quantitative field.\n Strong practical experience with probabilistic machine learning, Bayesian optimization, active learning, experimental design, or related approaches for sequential decision-making under uncertainty.\n Experience developing machine learning methods or systems for biological, biomedical, or experimental scientific data, with an ability to reason about noisy assays, sparse labels, experimental bias, and data-generation strategy.\n Demonstrated ability to translate ML ideas into systems, tools, or workflows that affect real scientific, experimental, or product decisions.\n Strong Python skills and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.\n Strong systems thinking and ability to design technical interfaces, reason about system tradeoffs, and partner with engineering teams to build scalable, maintainable ML infrastructure.\n Excellent communication skills and ability to bridge ML, engineering, protein design, and experimental stakeholders.\n Pragmatic, collaborative working style, with the ability to bring structure to open-ended problems and balance scientific rigor with execution in fast-moving, cross-functional environments.\n \n Nice to have \n \n Experience in protein design, protein engineering, antibody engineering, biologics discovery, or drug development.\n Experience partnering with experimental teams on design-build-test-learn cycles, high-throughput screening, directed evolution, pooled libraries, or model-guided experimental campaigns.\n Experience with multi-objective optimization, uncertainty calibration, model-guided library design, or experimental campaign planning.\n Experience developing and applying deep learning models, including transformer-based architectures\n Experience building or applying LLM agents, scientific copilots, or agentic syste","salary_min":192000,"salary_max":265000,"location":"Somerville, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","healthcare","code-generation","deep-learning","pytorch","machine-learning"],"apply_url":"https://generatebiomedicines.com/open-positions?gh_jid=4696856006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T19:16:51Z","expires_at":"2026-08-14T14:15:56.335011Z","created_at":"2026-07-15T14:15:56.439634Z","updated_at":"2026-07-15T14:15:56.439634Z","company_name":"Generate Biomedicines","company_slug":"generate-biomedicines","company_logo_url":"https://www.google.com/s2/favicons?domain=generatebiomedicines.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dbf3ed2d-61a8-46d4-8f99-13cd04d28f0e"},{"id":"f115ce97-f6c4-4c2d-9602-6a9e48528e12","company_id":"b6db41bc-ba14-4906-b2f7-a3ce9289a346","title":"Software Engineer, AI Platform","slug":"software-engineer-ai-platform-305af12e","description":"WHO WE ARE\n\nNotion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion.\n\n\n\nNotinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work.\n\n\n\n\nABOUT THE ROLE:\n\nMillions of people use Notion — and this number is increasing every day. That means millions of people trust us to deliver a fast, reliable, and secure experience, and we value this more than anything. We want to keep earning trust, while also continuing to amaze our users with the tools they can build in Notion.\n\nThe AI Platform team is responsible for building the shared foundations that let Notion ship AI products quickly and operate them safely at scale. You’ll join a team of talented engineers focused on making speed and quality compatible: reliability and availability through provider changes, quality and correctness systems like evals and release gates, observability that makes failures explainable, and shared primitives for model integrations, context management, long-running actions, and cost/performance tradeoffs. Notion’s AI platform is vital to helping product teams move faster with production-grade guardrails as models, providers, and AI capabilities rapidly evolve.\n\nThis role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work alongside the team during those days.\n\n\n\n\nWHAT YOU'LL ACHIEVE:\n\n - You'll own and play a pivotal role in the prototyping, development and scaling of systems and core AI platform primitives.\n\n - You’ll partner closely with product teams to provide paved paths and production-ready guardrails that help new AI features ship faster with less duplicated work.\n\n - You’ll work across infrastructure, shared libraries, APIs, and product integration points to make AI platform capabilities easy to adopt and high-leverage across Notion.\n\n - You’ll operate critical AI systems in production, using observability and diagnostics to understand provider/model behavior, debug failures, improve latency and cost, and evolve systems with minimal user disruption.\n\n - You’ll help Notion safely adopt new models, providers, and AI capabilities through versioning, controlled rollouts, compatibility layers, and clear quality/reliability gates.\n\n\n\n\nSKILLS YOU'LL NEED TO BRING:\n\n - Passion for AI systems at scale: You’ve worked on LLM, ML platform, data, or infrastructure teams that own critical shared systems. You understand the challenges of scaling reliability, latency, cost, and quality as usage and model complexity grow. You care deeply about building platforms that are dependable, efficient, and easy for other engineers to use.\n\n - Adaptable and curious: You like going deep on how systems behave in practice, especially when models, providers, and product requirements are changing quickly. You’re eager to use AI tools to work smarter and are willing to move across backend, infrastructure, libraries, and product code when that’s what the problem requires.\n\n - Extreme ownership: You’re comfortable working across ambiguous problem spaces, aligning stakeholders around a clear path forward, and driving execution with accountability. You take ownership of platform outcomes including reliability, quality, adoption, and operational follow-through beyond team boundaries.\n\n - Thoughtful problem-solving: For you, problem-solving starts with a clear and accurate understanding of the context. You can decompose ambiguous system behavior, debug across layers, and work toward clean, pragmatic solutions by yourself or with teammates. You’re comfortable asking for help when you get stuck.\n\n - Pragmatic and business-oriented: You understand that AI platform work is full of tradeoffs across quality, latency, cost, reliability, and speed of execution. You prioritize based on product and business impact, balancing craft with urgency and operational simplicity.\n\n\n\n\nNICE TO HAVES:\n\n - 2-4 years of experience as a Software Engineer\n\n - Experience with applied AI product development (prompting, evals, model integrations, or quality measurement).\n\n - You've built out and scaled data processing pipeli","salary_min":180000,"salary_max":201000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"junior","tags":["agents","mlops","llm","data-pipeline","platform"],"apply_url":"https://jobs.ashbyhq.com/notion/a9d4a192-d31c-48d2-8156-e2a75d98eec1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T14:23:31.706Z","expires_at":"2026-08-14T14:04:38.933673Z","created_at":"2026-07-15T14:04:39.064153Z","updated_at":"2026-07-15T14:04:39.064153Z","company_name":"Notion","company_slug":"notion","company_logo_url":"https://www.google.com/s2/favicons?domain=notion.so\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f115ce97-f6c4-4c2d-9602-6a9e48528e12"},{"id":"02fdc710-8e20-40fd-aedd-05f740fa50ac","company_id":"377b9ca2-ac79-48a5-8657-da630f9e447d","title":"Senior Staff / Principal Machine Learning Scientist, AI Inference \u0026 Optimization","slug":"senior-staff-principal-machine-learning-scientist-ai-inference-optimization-8c8ecaa7","description":"About Netskope \n Today, there's more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security. \n \n Since 2012, we have built the market-leading cloud security company and an award-winning culture powered by hundreds of employees spread across offices in Santa Clara, St. Louis, Bangalore, London, Paris, Melbourne, Taipei, and Tokyo. Our core values are openness, honesty, and transparency, and we purposely developed our open desk layouts and large meeting spaces to support and promote partnerships, collaboration, and teamwork. From catered lunches and office celebrations to employee recognition events and social professional groups such as the Awesome Women of Netskope (AWON), we strive to keep work fun, supportive and interactive.     Visit us at  Netskope Careers. Please follow us on LinkedIn and Twitter @Netskope . \n Positions are available at Senior Staff and above. Candidates are assessed individually and leveled according to their specific skills and background. \n About the role\n As a Senior Staff Machine Learning Scientist, you own the inference and optimization layer that makes AI in agentic workflows fast, efficient, and production-grade. You fine-tune and evaluate models, push latency and throughput on real hardware, and build the runtime that executes bounded AI tasks, validated against usage from Netskope’s large customer base so you optimize where the data points, not where you guess.\n What’s in it for you\n \n High-impact ownership. You own the model layer of a net-new product that changes the performance and economics of agentic AI.\n Cutting-edge, unusual stack. The hard, interesting inference problems live here: quantization, KV-cache and memory management, sparsity, fine-tuning, and hardware acceleration under real-world resource constraints.\n Real scale to build against. Netskope’s customer footprint gives you production signals most teams never see, so you deploy, validate, and iterate fast.\n \n What you will be doing\n \n Build and optimize the model inference path : quantization, KV-cache optimization, batching, and latency/memory/throughput tuning on constrained, commodity hardware.\n Fine-tune and evaluate models for bounded tasks; build eval harnesses that gate a capability to release on real accuracy, latency, and security relevance.\n Design and grow the task execution runtime (bounded sub-agents), pushing toward dynamic task generation and context compaction.\n Drive hardware acceleration / sparsity and support for larger models as the platform matures.\n Partner with the systems and backend engineers to ship capabilities end-to-end and iterate on real production signals.\n \n Required skills and experience\n \n 10+ years of overall industry experience , with 4+ years hands-on in ML/AI (model development, fine-tuning, and inference optimization).\n Hands-on with fine-tuning (e.g. LoRA/QLoRA), quantization (GGUF/AWQ/GPTQ), and inference runtimes (vLLM/SGLang, TensorRT-LLM, ONNX Runtime, llama.cpp, or MLX/CoreML). On-device or edge inference experience is a strong plus.\n Strong Python; comfort reaching into C++ for low-level interop is a plus.\n Solid grasp of transformer internals and the levers that move real inference performance and cost: KV cache, attention, batching, memory footprint.\n Fluency with agentic coding systems and genuine curiosity about agent harnesses like Claude Code, Pi, and Codex , so you should already be building with them, or itching to.\n Clear communication: able to distill a model or infra bottleneck into an actionable concept for cross-functional teammates.\n \n Education\n \n MS in Computer Science, Machine Learning, Electrical Engineering, or equivalent technical degree required, with a focus in AI/ML research; PhD in a related field strongly preferred.\n Compensation:  \n At Netskope, salary is one component of our competitive total rewards package. The salary range for this position is as listed below. This is a national range. For purposes of complying with applicable laws, the range applies to candidates in California, Colorado, Illinois, Maryland, New York, Washington, and other states. \n The successful candidate’s starting pay will also be determined based on job-related skills, experience, qualifications, location, and market conditions.  \n For all sales roles, the posted salary range is the On Target Earnings (OTE) range for the role, which is the sum of base salary and target commission amount at 100% goal achievement. \n In addition to salary, candidates may be eligible for other forms of compensation such as participation in a bonus plan (for non-sales roles) and a stock award program. Candidates may also be eligible for a comprehensive health plan and other benefits that can be reviewed at  Netskope Benefits site .","salary_min":182500,"salary_max":260500,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["fine-tuning","agents","llm","cloud","machine-learning","inference"],"apply_url":"https://www.netskope.com/company/careers/open-positions/?gh_jid=8063869","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T04:20:32Z","expires_at":"2026-08-14T14:11:38.941823Z","created_at":"2026-07-15T14:11:39.076302Z","updated_at":"2026-07-15T14:11:39.076302Z","company_name":"Netskope","company_slug":"netskope","company_logo_url":"https://www.google.com/s2/favicons?domain=netskope.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/02fdc710-8e20-40fd-aedd-05f740fa50ac"},{"id":"c2d1990a-6a3b-4236-9209-26c9f4b3c2e0","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Staff Product Manager, Infrastructure","slug":"staff-product-manager-infrastructure-d7875890","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. With 1500+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nAs a Product Manager on the Core Platform team at Harvey, you'll own the strategy, roadmap, and execution for the infrastructure that powers every user interaction with our global legal AI platform. Harvey serves the world's leading legal teams, processing trillions of tokens and millions of daily requests, and your work will shape how that capability reaches our users.\n\nYou'll operate at the intersection of deep customer need and hard technical constraints, translating the workflows of lawyers and other professionals into product requirements that engineering can build against. You'll balance ambitious, zero-to-one product bets with the operational discipline required to keep a mission-critical platform reliable, scalable, and secure as we expand across products, regions, and customers. Your decisions will directly influence adoption, retention, and the trust that our enterprise customers place in Harvey.\n\n\n\n\nWHAT YOU'LL DO\n\nYou'll partner directly with our Head of Infrastructure to define and drive the product vision and roadmap for a core area of the Harvey platform, aligning it with company strategy and grounding it in evidence from customers (external and internal), data, and the market. You'll work closely with engineering, product, and go-to-market teams to ship high-quality products on a predictable cadence, and you'll own the outcomes those products produce.\n\nDay to day, you will own the entire infrastructure planning, prioritization, and roadmapping. You’ll make and communicate crisp prioritization decisions, balancing new capabilities against reliability, performance, and security. You'll define the metrics that matter for your area — adoption, engagement, quality, and business impact — and hold the team accountable to them. You'll also serve as the connective tissue across functions, ensuring that customer feedback, competitive dynamics, and technical realities all inform the product direction, and you'll raise the product bar across the organization through rigorous specs, reviews, and decision-making. Some projects include architecting multi-region deployment strategies, developing comprehensive observability infrastructure, and more.\n\n\n\n\n\n\n\nWHAT YOU HAVE\n\n - 6+ years of product management experience shipping and scaling software platforms in a production environment, with a track record of measurable impact\n\n - Experience owning complex, technical products end to end, including platform, infrastructure, or AI/ML capabilities\n\n - Strong ability to translate ambiguous problems and deep customer needs into clear strategy, crisp requirements, and prioritized roadmaps\n\n - Fluency working with engineering and design teams on technical trade-offs, and comfort engaging with concepts like distributed systems, APIs, and cloud infrastructure at a level sufficient to make informed decisions\n\n - Excellent analytical skills, with the ability to define metrics and use data to guide decisions\n\n - Outstanding written and verbal communication, and a demonstrated ability to influence and align stakeholders across functions\n\n - A high bar for quality, strong product judgment, and a \"spidey sense\" for where a product experience could break down\n\nNice to Have\n\n - Experience building products for legal, professional-services, or other expert users with demanding accuracy and trust requirements\n\n - Background with AI/ML products, LLM-powered applications, or high-throughput inference systems\n\n - Experience with multi-tenant, enterprise platforms subject to strict security and compliance requirements\n\n - Prior experience partnering closely with infrastructure or platform engineering teams\n\n - A prior career in law or another professional-services field, or e","salary_min":213600,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","cloud","llm","agents","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/harvey/d629fa64-599d-435c-b4ef-a925299ddac8/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T00:04:08.326Z","expires_at":"2026-08-14T14:02:50.722292Z","created_at":"2026-07-15T14:02:50.87055Z","updated_at":"2026-07-15T14:02:50.87055Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c2d1990a-6a3b-4236-9209-26c9f4b3c2e0"},{"id":"390fffaf-6a9c-47f1-b56c-cd3a51ddec12","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Senior Engineering Manager, Production Engineering","slug":"senior-engineering-manager-production-engineering-18d99048","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. With 1500+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nHarvey is building the AI platform trusted by the world's leading law firms and enterprises. Our infrastructure is the foundation that powers every customer interaction, every model inference, and every production workload.\n\nWe're looking for a Senior Engineering Manager to lead our Infrastructure Foundation \u0026 Production Quality Engineering organization. This team is responsible for building and operating Harvey's core compute and networking infrastructure, Kubernetes platform, workflow orchestration platform, and production infrastructure foundations that enable engineering teams to move quickly with confidence.\n\nIn this role, you'll own the reliability, scalability, security, and efficiency of Harvey's infrastructure platform. You'll lead a team of high-performing engineers responsible for compute fleet management, capacity planning, infrastructure automation, and production operations. You'll partner closely with Product Engineering, Security, AI Infrastructure, and Platform teams to ensure our infrastructure scales with Harvey's rapid growth.\n\nYou'll report to the Head of Infrastructure and play a key leadership role in shaping the future of Harvey's infrastructure platform.\n\nAt Harvey, we value Decisiveness, Simplicity, and the belief that Job's Not Finished. We move quickly, prioritize clarity, and continuously raise the bar for engineering excellence.\n\n\n\n\nWHAT YOU'LL DO\n\n\nLEADERSHIP \u0026 STRATEGY\n\n - Lead, mentor, and grow a team of high-performing infrastructure engineers responsible for Harvey's production infrastructure foundation.\n\n - Foster a culture of operational excellence, engineering quality, customer ownership, and continuous improvement.\n\n - Partner with Engineering, Security, Product, and AI Infrastructure leaders to define long-term infrastructure strategy and execution priorities.\n\n - Drive technical direction for compute infrastructure, networking, Kubernetes, workflow orchestration, and production operations.\n\n - Lead cross-functional initiatives to improve reliability, scalability, security, operational efficiency, and infrastructure cost optimization.\n\n\nINFRASTRUCTURE FOUNDATION \u0026 PRODUCTION OPERATIONS\n\n - Own and operate Harvey's global compute and network infrastructure, ensuring high availability, scalability, reliability, and performance.\n\n - Manage compute resources to maximize utilization, performance, and service availability while supporting rapidly growing AI workloads.\n\n - Lead capacity planning, demand forecasting, and fleet lifecycle management to ensure infrastructure scales efficiently with business growth.\n\n - Operate and continuously improve Harvey's Kubernetes platform, including cluster provisioning, upgrades, monitoring, reliability, performance, and operational automation.\n\n - Own Harvey's Temporal-based workflow orchestration platform, ensuring reliable, scalable, and observable execution of distributed application workflows.\n\n - Drive infrastructure cost optimization through capacity management, resource rightsizing, workload efficiency improvements, and utilization monitoring.\n\n - Build and maintain secure infrastructure foundations, including identity and access management, network isolation, secrets management, auditing, and compliance controls.\n\n - Develop scalable Infrastructure-as-Code and automation frameworks using technologies such as Terraform and Pulumi.\n\n - Establish comprehensive observability, monitoring, alerting, incident response, and operational readiness practices across the infrastructure platform.\n\n\n\n\nWHAT YOU HAVE\n\n - 7+ years of software or infrastructure engineering experience, including 5+ years leading engineering teams.\n\n - Deep expertise operating large-scale cloud infrastructure on","salary_min":272000,"salary_max":355000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","llm","cloud","agents"],"apply_url":"https://jobs.ashbyhq.com/harvey/8e420b36-6711-49dd-8a64-f246270af7d3/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T23:51:16.213Z","expires_at":"2026-08-14T14:02:47.991444Z","created_at":"2026-07-15T14:02:48.123991Z","updated_at":"2026-07-15T14:02:48.123991Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/390fffaf-6a9c-47f1-b56c-cd3a51ddec12"},{"id":"b5fee987-f2ea-4b80-a04f-395e616158d8","company_id":"c93e0284-9c76-4a85-9905-494865ab9278","title":"AI Systems Performance Engineer - New Graduate","slug":"ai-systems-performance-engineer-new-graduate-e4bfa2f7","description":"The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. \n SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets. \n About The Role \n We are seeking a talented and highly motivated AI Systems Performance Engineer to bring up and optimize state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform.\n You'll work hands-on with advanced AI models — such as DeepSeek, GLM, Kimi, GPT OSS, Llama, Qwen, and other frontier architectures — and learn how modern AI systems achieve high throughput, low latency, and efficient large-scale inference.\n In this role, you'll work at the intersection of machine learning and computer systems, collaborating with engineers across model, compiler, runtime, and hardware teams. This is an ideal opportunity for a new graduate who is passionate about understanding how AI models execute on real hardware and wants to help build the next generation of high-performance AI systems.\n Responsibilities \n \n Bring up cutting-edge foundation models, including LLMs and multimodal models, on the SambaNova platform through the SambaNova software stack.\n Analyze and profile model execution to identify performance bottlenecks across model, compiler, runtime, and hardware layers.\n Optimize AI workloads for throughput, latency, memory efficiency, and scalability.\n Collaborate with machine learning, compiler, runtime, and hardware engineers to develop high-performance AI applications.\n Explore and integrate new techniques in model architecture, quantization, scheduling, caching, and memory optimization.\n Develop tools, benchmarks, and performance analysis methodologies for large-scale AI inference.\n Investigate new model architectures and translate research advances into efficient implementations on production AI systems.\n Contribute ideas for dataflow, scheduling, and system optimizations for both single-node and distributed inference.\n \n Basic Qualifications \n \n Bachelor's or Master's degree in computer science, electrical engineering, computer engineering, or a related technical field (e.g., applied mathematics, physics, or statistics), completed or expected before the start date.\n Strong programming skills in Python, C++, or a similar programming language.\n Solid foundations in algorithms, data structures, computer architecture, operating systems, or parallel computing.\n Familiarity with deep learning and at least one major ML framework, such as PyTorch, TensorFlow, or JAX.\n Strong analytical and problem-solving skills, with an interest in understanding and optimizing system performance.\n Ability and enthusiasm to learn across machine learning, software systems, and hardware.\n \n Preferred Qualifications \n \n Coursework, research, internship, or project experience in machine learning systems, computer architecture, compilers, distributed systems, or high-performance computing.\n Hands-on experience with LLMs, multimodal models, or transformer architectures.\n Familiarity with model inference, KV cache, batching, quantization, or distributed execution.\n Experience with GPU or accelerator programming using CUDA, Triton, OpenCL, or similar technologies.\n Familiarity with frameworks such as vLLM, DeepSpeed, Megatron, or TensorRT.\n Understanding of memory hierarchy, caching, parallelism, or scheduling.\n Experience profiling and optimizing the performance of software or ML workloads.\n Research publications, open-source contributions, programming competitions, or technically challenging personal projects are a plus.\n \n We value strong technical fundamentals, curiosity, and the ability to learn quickly. Prior production experience with large-scale AI systems is not required.\n Base Salary Range:\n Base Pay Range\n $135,000 — $165,000 USD \n Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.  \n EEO Policy SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex ","salary_min":135000,"salary_max":165000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["llm","gpu","distributed-systems","deep-learning","tensorflow","generative-ai","pytorch"],"apply_url":"https://sambanova.ai/sambanova-available-positions/?gh_jid=6115124004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:28:28Z","expires_at":"2026-08-14T14:06:10.228422Z","created_at":"2026-07-15T14:06:10.360035Z","updated_at":"2026-07-15T14:06:10.360035Z","company_name":"SambaNova Systems","company_slug":"sambanova","company_logo_url":"https://www.google.com/s2/favicons?domain=sambanova.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b5fee987-f2ea-4b80-a04f-395e616158d8"},{"id":"e6cf414e-5202-4a66-9735-43bcbeb83352","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Senior Machine Learning Engineer, Ads Content Understanding","slug":"senior-machine-learning-engineer-ads-content-understanding-06e45727","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Reddit has a flexible workforce!  If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence.\n Ads Content Understanding (ACU) owns and produces signals that describe what Reddit content is about, how brand safe and suitable it is, and what users are trying to accomplish in commercial conversations. ACU is responsible for:\n \n The Knowledge Graph (entities, brands, products, and relationships across Reddit and external sources).\n Content taxonomies such as IAB, Shopify Standard Product Taxonomy, IAS, and other commercial taxonomies used for targeting, safety, and marketplace dynamics.\n Opinion mining for ads use cases: sentiment, stance, commercial intent, and other qualitative attributes of conversations.\n Shopping / product understanding: detecting product entities, product categories, and product attributes in organic conversations and aligning them with shopping catalogs.\n Signals and tags registry: a unified, governed catalog of ACU signals that powers retrieval, ranking, safety, and insights across Ads Foundations and partner teams. \n \n We are looking for a Senior Machine Learning Engineer (IC4) who will act as a key contributor to the Content Understanding roadmap for the Monetization org.\n This is not a research scientist or pure DS role; success is defined by robust, shipped systems and monetization impact.  The ideal candidate is a pragmatic engineer with strong software engineering fundamentals and solid ML intuition—not a pure research scientist. This is an Applied MLE role, requiring someone who can evaluate when to leverage hosted LLMs versus custom models, help scale content understanding to new modalities (e.g., video), and drive practical ML solutions that deliver business impact. \n Responsibilities: \n \n Operate across the full ML lifecycle (problem framing, data, modeling, evaluation, deployment, monitoring, and oncall), designing scalable ML pipelines and championing responsible AI (bias, safety, explainability) for ACU’s models and signals in production.\n Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU, design reviews, setting technical standards, and uplifting the team’s modeling and systems craft.\n Develop evaluation systems and quality monitoring systems for content understanding signals, using SOTA LM-judge practices. \n Drive operational excellence for ACU’s ML systems by defining SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost)\n Build and evolve content understanding capabilities for commercial conversations (e.g., reviews vs. recommendations vs. comparisons vs. Q\u0026A; sentiment and stance; product entities and categories) and operationalize them as robust signals that power contextual and shopping ads, auto-targeting, new formats, and insights products.\n Drive LLM and modern ML best practices within ACU: define when to prompt, finetune, or distill; design evaluation and safety harnesses; and lead at least one major distillation effort to replace external APIs with in-house models.\n \n Required Qualifications: \n \n 5+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains, or Ads. \n Demonstrated Senior-level technical leadership: has contributed to architecture decisions, standards, and design reviews in their immediate team\n Strong communication skills, with the ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams, especially in ambiguous, cross-team problem spaces like Seekers/Searchers monetization. \n Some experience building and shipping NLP / Language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models), with clear business outcomes (e.g., CTR/ROAS uplift, safety improvements). Experience with commercial or intent modeling is a strong plus.\n \n Preferred Qualifications: \n \n Practical experience using LLMs in production for labeling, evaluation, or distillation (e.g., LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs.\n Significant experience with PyTorch, TensorFlow, or similar, and production-quality code in Python (and ideally one staticall","salary_min":216700,"salary_max":303400,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["nlp","healthcare","tensorflow","llm","pytorch","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8008648","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:21:59Z","expires_at":"2026-08-14T14:10:37.134107Z","created_at":"2026-07-15T14:10:37.261524Z","updated_at":"2026-07-15T14:10:37.261524Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e6cf414e-5202-4a66-9735-43bcbeb83352"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":2663,"total_pages":134}