{"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":"f47b2b52-9138-4056-a197-783873a96c39","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Voice Model","slug":"member-of-technical-staff-voice-model-5b5f6cb9","description":"SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.  Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \n ABOUT THE ROLE:\n You will join the Grok Voice Model team to help build the world’s best voice AI. We deliver smooth, natural, low-latency spoken interactions — expressive, multilingual, and reliable across devices and real-time scenarios. We own the full training pipeline: massive data curation, premium audio processing, frontier speech-language pre-training, and intensive post-training to push quality, speed, and stability to the limit.\n Our goal: make talking to AI feel like conversing with the most charming, kind, and knowledgeable person imaginable. We’re seeking exceptionally smart, execution-oriented engineers to help us get there.\n RESPONSIBILITIES:\n \n Design and execute large-scale speech data curation and processing pipelines, including collection of diverse real-world audio, synthetic data generation, and automated annotation workflows to enable high-quality model training and evaluation.\n Work on pre-training and post-training of speech-language models, with targeted enhancements through supervised fine-tuning, reinforcement learning, and other techniques to ensure Grok Voice responses are accurate, factually grounded, natural and idiomatic in spoken style, conversational in tone, and fluent across multiple languages.\n Build and iterate a comprehensive evaluation framework covering objective metrics (accuracy, quality, latency, expressiveness), human preference studies, content factuality assessments, real-time interaction quality, and experimentation infrastructure to measure and improve performance.\n Work closely with product teams to integrate voice models into applications and real-time environments, define spoken interaction specifications, and handle the full lifecycle from prototype to global-scale deployment for stable, low-latency, delightful voice experiences.\n \n BASIC QUALIFICATIONS:\n \n Python expert with deep proficiency in writing clean, efficient code for AI/ML systems.\n Hands-on experience processing large-scale datasets using tools like Spark and Ray for cleaning, augmentation, and feature extraction.\n Proficiency in pre-training and post-training speech-language models using JAX/PyTorch, including supervised fine-tuning, reinforcement learning, and optimizations for accuracy, factuality, natural spoken style, detail, and multilingual fluency.\n Ability to set up and run rigorous evaluation pipelines: objective metrics, human preference studies, content factuality checks, and iterative A/B testing to drive model improvements.\n Experience building or working with large-scale distributed training and inference systems on Kubernetes.\n Proactive, self-driven attitude — ready to grind in a fast-paced, high-caliber team to deliver outstanding voice AI experiences.\n \n COMPENSATION AND BENEFITS:\n $150,000 - $450,000 USD\n Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n SpaceXAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice .","salary_min":150000,"salary_max":450000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["speech","fine-tuning","reinforcement-learning","distributed-systems","pytorch","pre-training"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5051966007","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-16T20:39:18Z","expires_at":"2026-08-14T14:04:44.897369Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-07-15T14:04:45.027875Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f47b2b52-9138-4056-a197-783873a96c39"},{"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":"8c402485-1400-4e3b-aacf-eaa1ab3b5dfb","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Research Engineer - Distributed Training","slug":"research-engineer-distributed-training-19cda6e4","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\nWe train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Semianalysis, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\nWHAT YOU’LL WORK ON\n\n - Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/prime-rl framework.\n\n - Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.\n\n - Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.\n\n - Work on distributed training systems spanning data, tensor, and pipeline parallel workloads.\n\n - Help shape the architecture of our RL training stack, including async rollout and post-training systems.\n\n - Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.\n\n - Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.\n\n - Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.\n\n\n\n\n\nYOU MAY BE A FIT IF YOU HAVE\n\n - Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.\n\n - Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.\n\n - Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.\n\n - Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.\n\n - Strong understanding of GPU architecture, profiling, and performance debugging.\n\n - Ability to identify bottlenecks across the stack and drive improvements from first principles.\n\n - Comfort working in a fast-moving environment with ambiguous problems and high ownership.\n\n\n\n\nESPECIALLY EXCITING\n\n - Experience writing or optimizing CUDA / Triton kernels.\n\n - Experience with compiler or runtime optimization for ML systems.\n\n - Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.\n\n - Experience with multi-node GPU clusters and high-performance networking.\n\n - Contributions to open-source ML systems or infrastructure projects.\n\n - Interest in publishing technical work or sharing insights through engineering blogs and technical writing.\n\n\n\n\n\n\n\nBENEFITS \u0026 PERKS\n\n - Cash Compensation Range of $150-350k, plus equity incentives, aligning your success with the growth and impact of Prime Intellect.\n\n - Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.\n\n - Visa sponsorship and relocation assistance for international candidates.\n\n - Quarterly team off-sites, hackathons, conferences and learning opportunities.\n\n - Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.\n\nIf you’re excited about building the systems foundation for frontier-scale training and open superintelligence, we’d love to hear from you.","salary_min":150000,"salary_max":350000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["llm","pytorch","distributed-systems","pre-training","agents","gpu","search","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/8bd52610-175c-42a7-a7cd-b29c45f9d305/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:43:34.749Z","expires_at":"2026-08-14T14:12:06.052408Z","created_at":"2026-04-13T15:01:32.550978Z","updated_at":"2026-07-15T14:12:06.185751Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8c402485-1400-4e3b-aacf-eaa1ab3b5dfb"},{"id":"5b4c2841-d819-4ab1-87e4-988c9bff0235","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Software Engineer, Identity","slug":"senior-software-engineer-identity-542360e2","description":"Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.\n At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.\n At the foundation of these products is the Identity  Engineering team.  In this role, you will help support the design and development of core software systems specifically focused on identity, access management, authorization, and authentication.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.\n You will:\n \n Drive the design, and implementation of our identity infrastructure to ensure secure authentication and authorization across enterprise systems.\n Build software for authentication mechanisms such as Single Sign-On (SSO), Multi-Factor Authentication (MFA), and federated identity solutions (SAML, OAuth, OpenID Connect).\n Build software for authorization mechanisms such as Relation-based access control (ReBAC), Attribute-based access control (ABAC), Role-based access control (RBAC).\n Build software-defined identity governance policies to ensure compliance with security policies, industry regulations (e.g., NIST, SOC2, ISO 27001), and organizational standards.\n Present technical information to teams and stakeholders, providing guidance and insight on identity management and best practices.\n \n Ideally you’d have:\n \n 5+ years of full-time engineering experience, post-graduation with specialities in infrastructure and identity systems.\n Infrastructure expertise – IAM controls, Infrastructure as Code (Terraform, Pulumi), microservice deployment best practices.\n Hands-on experience working with OpenFGA, Authzed, Cedar, Topaz, or similar authorization frameworks at scale.\n Strong understanding of Zanzibar-based ReBAC models, relationship tuples, and access control evaluation.\n Strong knowledge of authentication standards such as OAuth 2.0, OIDC, SAML, and JWT, as well as industry standard IdP solutions like EntraID, Okta, etc.\n Extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred).\n Show a track record of independent ownership of successful engineering 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 Experience securing API access and implementing access control mechanisms at the application level.\n Multi-cloud infrastructure experience – AWS, Azure, GCP, and more.\n Proficiency in integrating IAM solutions with applications built using frameworks such as Java, Python, Node.js, or .NET.\n Mentorship/leadership experience supporting junior engineers\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, Seat","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","distributed-systems","generative-ai","cloud","llm","microservices","reinforcement-learning","pre-training"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4711898005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:08:31Z","expires_at":"2026-08-14T14:01:47.642927Z","created_at":"2026-07-09T14:01:29.84661Z","updated_at":"2026-07-15T14:01:47.810315Z","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/5b4c2841-d819-4ab1-87e4-988c9bff0235"},{"id":"f0473c5a-729d-405b-813d-b6c60cb2c44a","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Life Sciences","slug":"research-engineer-life-sciences-531dd7ae","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the Role \n We're seeking an exceptional Research Engineer to join our Life Sciences team at Anthropic. Our team is organized around the north star goal of accelerating progress in the life sciences, from early discovery through translation, by an order of magnitude. Our team likes to think across the whole model stack. In this role, you'll combine your deep expertise in machine learning engineering to develop novel evaluation frameworks and training strategies that push the frontier of what AI can achieve in biology.\n You'll work at the intersection of cutting-edge AI and the biological sciences, developing rigorous methods to measure and improve model performance on complex scientific tasks. You'll collaborate closely with world-class researchers and engineers to build AI systems that can engage in all phases of research and development, while maintaining our commitment to safety and beneficial impact.\n Previous experience in life sciences is welcome, but not required for this role.\n Minimum Qualifications \n \n Demonstrated experience training and evaluating large language models\n Proficiency in Python and familiarity with modern ML development practices\n Experience building and managing data pipelines for large-scale datasets\n Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments\n Strong written and verbal communication skills, with the ability to work independently while collaborating effectively across cross-functional teams\n \n Preferred Qualifications \n \n 8+ years of machine learning experience\n Prior work experience in AI and biology, including graduate studies (molecular biology, biochemistry, computational biology, or related fields)\n Experience working with large-scale biological datasets\n Published research or practical experience in scientific AI applications or long-horizon reasoning\n Background in reinforcement learning and/or pretraining\n Knowledge of containerization technologies (e.g., Docker, Kubernetes) and cloud deployment at scale\n Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing\n Contributions to open-source scientific software or databases\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 — $500,000 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 communication, don't click any links—visit  anthropic.com/careers  directly for confirmed position openings.\n How we're different \n We bel","salary_min":350000,"salary_max":500000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","search","data-pipeline","llm","reinforcement-learning","pre-training","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5265365008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-03T14:01:13Z","expires_at":"2026-08-14T14:00:28.221294Z","created_at":"2026-07-04T14:00:23.291795Z","updated_at":"2026-07-15T14:00:28.357858Z","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/f0473c5a-729d-405b-813d-b6c60cb2c44a"},{"id":"7076d892-7466-4fa7-947a-419a9fd28340","company_id":"a0000000-0000-0000-0000-000000000003","title":"Software Engineer, Identity","slug":"software-engineer-identity-c818ccb1","description":"Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.\n At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.\n At the foundation of these products is the Identity  Engineering team.  In this role, you will help support the design and development of core software systems specifically focused on identity, access management, authorization, and authentication.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.\n You will:\n \n Drive the design, and implementation of our identity infrastructure to ensure secure authentication and authorization across enterprise systems.\n Build software for authentication mechanisms such as Single Sign-On (SSO), Multi-Factor Authentication (MFA), and federated identity solutions (SAML, OAuth, OpenID Connect).\n Build software for authorization mechanisms such as Relation-based access control (ReBAC), Attribute-based access control (ABAC), Role-based access control (RBAC).\n Build software-defined identity governance policies to ensure compliance with security policies, industry regulations (e.g., NIST, SOC2, ISO 27001), and organizational standards.\n Present technical information to teams and stakeholders, providing guidance and insight on identity management and best practices.\n \n Ideally you’d have:\n \n 3+ years of full-time engineering experience, post-graduation with specialities in infrastructure and identity systems.\n Infrastructure expertise – IAM controls, Infrastructure as Code (Terraform, Pulumi), microservice deployment best practices.\n Hands-on experience working with OpenFGA, Authzed, Cedar, Topaz, or similar authorization frameworks at scale.\n Strong understanding of Zanzibar-based ReBAC models, relationship tuples, and access control evaluation.\n Strong knowledge of authentication standards such as OAuth 2.0, OIDC, SAML, and JWT, as well as industry standard IdP solutions like EntraID, Okta, etc.\n Extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred).\n Show a track record of independent ownership of successful engineering 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 Experience securing API access and implementing access control mechanisms at the application level.\n Multi-cloud infrastructure experience – AWS, Azure, GCP, and more.\n Proficiency in integrating IAM solutions with applications built using frameworks such as Java, Python, Node.js, or .NET.\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, Seattle is:\n $216,000 — $270,000 USD \n PLEASE NOTE:  Our policy","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","distributed-systems","cloud","pre-training","reinforcement-learning","fine-tuning","microservices","generative-ai"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4710484005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-30T20:51:01Z","expires_at":"2026-08-14T14:01:48.930602Z","created_at":"2026-07-01T14:01:23.481984Z","updated_at":"2026-07-15T14:01:49.070254Z","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/7076d892-7466-4fa7-947a-419a9fd28340"},{"id":"2d3dc650-dcfc-4532-93b8-8b3c42ec0fc2","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Machine Learning Engineer, Community Support Engineering","slug":"machine-learning-engineer-community-support-engineering-12766547","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 Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb. \n The Core ML team in Community Support is the team responsible for adopting the Agentic AI technologies to enable an intelligent, scalable and exceptional customer service experience. We are responsible for developing the Chat AI assistant, Voice AI Assistant and more! The team is constantly exploring the SOTA Agentic architecture, develops and enhances various AI models, ML services and leverages tools including SFT, Reinforcement learning, Distillation, RAG/Search,  LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. \n The Difference You Will Make: \n We believe our current customer experiences in these domains are only scratching the surface of the innovations that are possible, and that science is at the heart of delivering a step-function change for our Guest and and Host on Airbnb. You will build and leverage cutting edge AI technologies to transform Airbnb’s customer service by delivering personalized, easy-to-use and proactive customer service experience. Many of the initiatives you’ll tackle are in their early conceptual stages. You will have the opportunity to shape these ideas from inception to production, turning visionary concepts into impactful realities.\n A Typical Day:  \n \n Champion the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems\n Work cross-functionally with product, design, and other engineering counterparts to design and build efficient AI solutions for Airbnb CS products\n Learn and share the latest AI/ML technologies with the team.\n \n Your Expertise: \n \n (Required) PhD or 3+ YOE in Computer Science, Machine Learning, Statistics, Artificial Intelligence, or a related technical field — or equivalent industry experience\n Hands-on expertise in LLM, including pretraining, fine-tuning (SFT, RLHF, GRPO), prompt engineering, RAG architectures, and LLM evaluation frameworks\n Experience building Agentic AI systems — including multi-agent orchestration, tool-use, planning, memory, and autonomous reasoning pipelines (e.g., ReAct, LangGraph, AutoGen, or similar)\n Experience of shipping production-grade ML/AI systems at scale, with deep understanding of ML infrastructure, model serving, and MLOps best practices\n Excellent communication skills with the ability to collaborate effectively across Engineering, Product, and Design organizations\n \n Your Location: \n Due to the nature of this position, the successful applicant will need to be based in San Francisco-Bay Area, CA or Seattle, Washington to be able to conduct their work. Currently, employees can not be located in: Alaska, Indiana, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin, Alabama, Mississippi, Oklahoma, Delaware And Rhode Island. This list is continuously being updated, please check back with us if the state you live in is on the exclusion list.  If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. \n Our Commitment To Inclusion \u0026 Belonging: \n Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.\n We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process. \n We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application.\n How We'll Take Care of You: \n Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.   \n Pay Range\n $170,000 — $180,000 USD","salary_min":170000,"salary_max":180000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["rag","llm","reinforcement-learning","pre-training","mlops","speech","agents","fine-tuning"],"apply_url":"https://careers.airbnb.com/positions/8024267?gh_jid=8024267","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-23T21:05:19Z","expires_at":"2026-08-14T14:11:22.225345Z","created_at":"2026-06-28T14:09:03.008829Z","updated_at":"2026-07-15T14:11:22.376533Z","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/2d3dc650-dcfc-4532-93b8-8b3c42ec0fc2"},{"id":"3ff0bc99-7ec1-4627-96ef-86e258203813","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Staff Software Engineer, AI Runtime","slug":"staff-software-engineer-ai-runtime-2e3bacea","description":"P-1930 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.\n Training and customizing state-of-the-art AI models is one of the most demanding workloads in computing, and it sits at the heart of Databricks' Mosaic AI mission. AI Runtime (AIR) is our managed platform for large-scale GPU training and fine-tuning. It gives customers on-demand access to fleets of the latest accelerators and a serverless experience that hides the complexity of provisioning, scheduling, and orchestrating multi-node jobs, with the resilience to keep training running for days or weeks across thousands of GPUs. AIR powers the full spectrum of custom training, from fine-tuning open models to pre-training frontier-scale foundation models, for some of the most sophisticated AI teams in the world.\n As a Staff Software Engineer for AI Runtime, you will play a critical role in building and scaling the systems that make large-scale training fast, reliable, and effortless. You will drive the architecture and evolution of the managed GPU training stack, spanning scheduling and capacity, distributed training performance, fault tolerance, and the developer experience of launching and operating jobs at scale. Beyond hands-on contributions to core systems, you will help define the long-term technical vision for AIR, mentor senior engineers, partner across product, research, and platform teams, and lead the initiatives that expand the technical and business impact of custom training at Databricks.\n The impact you will have: \n \n Drive the architecture and evolution of AIR's managed GPU training platform, delivering scalable, high-throughput, and resilient training across fleets that span thousands of accelerators.\n Solve the hardest problems in large-scale training, including multi-node orchestration, distributed parallelism strategies, GPU scheduling and dynamic routing, high-throughput data loading, and checkpoint and restore for very long-running jobs.\n Push GPU efficiency and training performance, raising utilization (such as model FLOPs utilization and end-to-end throughput) and lowering cost per training run across diverse model architectures and hardware generations.\n Build the resilience and observability foundations that keep multi-node jobs healthy, detecting and recovering from hardware and software failures with minimal disruption to customers.\n Partner with product, research, and platform teams to shape the APIs, CLI, and developer experience that make it easy to launch, monitor, and debug production training jobs.\n Lead end-to-end engineering efforts, from design through production rollout, holding a high bar for performance, correctness, and reliability.\n Make direct, high-impact contributions to the core systems behind AIR, and help bring up support for the latest accelerators and new regions as the fleet grows.\n Champion engineering excellence, mentor other engineers through design reviews and technical discussions, and help shape Databricks' long-term technical direction in AI training infrastructure.\n \n  \n What we look for: \n \n 10+ years of experience building and operating large-scale distributed systems, with significant depth in GPU training infrastructure, high-performance computing, or ML systems.\n Hands-on experience with distributed training frameworks (such as PyTorch, FSDP, DeepSpeed, or Megatron) and the parallelism strategies (data, tensor, pipeline, and sequence parallelism) used to train large models.\n Strong understanding of training resilience patterns, including checkpointing, failure detection, and automatic recovery for long-running, multi-node jobs.\n Solid grasp of GPU performance fundamentals, including accelerator architecture, high-speed interconnects (such as NVLink and InfiniBand or RoCE), collective communication, and the bottlenecks that govern training throughput and utilization.\n Experience building and operating managed, multi-tenant platform products in the cloud, with clear SLAs and SLOs for availability, performance, and reliability.\n Strong foundation in algorithms, data structures, and system design as applied to performance-sensitive, large-scale distributed systems.\n Proven ability to deliver technically complex, high-impact initiatives that create clear customer or business value.\n Strong communication skills and the ability to collaborate across product, research, and infrastructure teams in a fast-moving environment.\n Strategic, product-oriented mindset with the ability to align technical execution to a long-term vision, and a passion for mentoring engineers and fostering technical excellence.\n BS in Computer Science or a related field (MS or PhD preferred).\n \n  \n  \n Pay Ran","salary_min":190000,"salary_max":265000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","pre-training","distributed-systems","pytorch","fine-tuning","data-pipeline"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8582271002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-08T21:32:45Z","expires_at":"2026-08-14T14:02:36.047878Z","created_at":"2026-06-28T14:02:15.882818Z","updated_at":"2026-07-15T14:02:36.180476Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3ff0bc99-7ec1-4627-96ef-86e258203813"},{"id":"9726ecea-9913-45ac-9639-e77847699045","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Senior Software Engineer, AI Runtime","slug":"senior-software-engineer-ai-runtime-216c21ab","description":"P-1428\n At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.\n Training and customizing state-of-the-art AI models is one of the most demanding workloads in computing, and it sits at the heart of Databricks' Mosaic AI mission. AI Runtime (AIR) is our managed platform for large-scale GPU training and fine-tuning. It gives customers on-demand access to fleets of the latest accelerators and a serverless experience that hides the complexity of provisioning, scheduling, and orchestrating multi-node jobs, with the resilience to keep training running for days or weeks across thousands of GPUs. AIR powers the full spectrum of custom training, from fine-tuning open models to pre-training frontier-scale foundation models, for some of the most sophisticated AI teams in the world.\n As a Senior Software Engineer for AI Runtime, you will play a critical role in building and scaling the systems that make large-scale training fast, reliable, and effortless. You will drive the architecture and evolution of the managed GPU training stack, spanning scheduling and capacity, distributed training performance, fault tolerance, and the developer experience of launching and operating jobs at scale. Beyond hands-on contributions to core systems, you will help shape the technical direction for AIR, mentor other engineers, partner across product, research, and platform teams, and contribute to the initiatives that expand the technical and business impact of custom training at Databricks.\n The impact you will have: \n \n Drive the architecture and evolution of AIR's managed GPU training platform, delivering scalable, high-throughput, and resilient training across fleets that span thousands of accelerators.\n Solve the hardest problems in large-scale training, including multi-node orchestration, distributed parallelism strategies, GPU scheduling and dynamic routing, high-throughput data loading, and checkpoint and restore for very long-running jobs.\n Push GPU efficiency and training performance, raising utilization (such as model FLOPs utilization and end-to-end throughput) and lowering cost per training run across diverse model architectures and hardware generations.\n Build the resilience and observability foundations that keep multi-node jobs healthy, detecting and recovering from hardware and software failures with minimal disruption to customers.\n Partner with product, research, and platform teams to shape the APIs, CLI, and developer experience that make it easy to launch, monitor, and debug production training jobs.\n Lead end-to-end engineering efforts, from design through production rollout, holding a high bar for performance, correctness, and reliability.\n Make direct, high-impact contributions to the core systems behind AIR, and help bring up support for the latest accelerators and new regions as the fleet grows.\n Champion engineering excellence, mentor other engineers through design reviews and technical discussions, and contribute to Databricks' technical direction in AI training infrastructure.\n \n  \n What we look for: \n \n 5+ years of experience building and operating large-scale distributed systems, with experience in GPU training infrastructure, high-performance computing, or ML systems.\n Experience with distributed training frameworks (such as PyTorch, FSDP, DeepSpeed, or Megatron) and the parallelism strategies (data, tensor, pipeline, and sequence parallelism) used to train large models.\n Strong understanding of training resilience patterns, including checkpointing, failure detection, and automatic recovery for long-running, multi-node jobs.\n Solid grasp of GPU performance fundamentals, including accelerator architecture, high-speed interconnects (such as NVLink and InfiniBand or RoCE), collective communication, and the bottlenecks that govern training throughput and utilization.\n Experience building and operating managed, multi-tenant platform products in the cloud, with clear SLAs and SLOs for availability, performance, and reliability.\n Strong foundation in algorithms, data structures, and system design as applied to performance-sensitive, large-scale distributed systems.\n Proven ability to deliver technically complex, high-impact initiatives that create clear customer or business value.\n Strong communication skills and the ability to collaborate across product, research, and infrastructure teams in a fast-moving environment.\n Customer-focused mindset with the ability to align implementation details with product goals, and a passion for mentoring engineers and fostering technical excellence.\n BS in Computer Science or a related field (MS or PhD preferred).\n  \n Pay Range Transparency \n Databricks is committ","salary_min":160000,"salary_max":225000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","pytorch","fine-tuning","generative-ai","pre-training","data-pipeline"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8582276002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-08T21:32:38Z","expires_at":"2026-08-14T14:02:31.57595Z","created_at":"2026-06-28T14:02:12.287649Z","updated_at":"2026-07-15T14:02:31.712743Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9726ecea-9913-45ac-9639-e77847699045"},{"id":"4a0ffe7d-807a-4b2f-adf3-b421a54fbf3a","company_id":"3c528ec7-088e-499c-b3ea-9926667c7188","title":"Senior Research Scientist, Machine Learning (BioFM)","slug":"senior-research-scientist-machine-learning-biofm-c9f00afe","description":"About Us\nDeep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of RNA biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team in Toronto and Cambridge, MA is revolutionizing how new medicines are created.\nOpportunity\nWe are seeking an exceptional and creative Senior/Staff Machine Learning Scientist to lead and innovate within our core AI research team, specifically focusing on the creative building of Biological Foundation Models (BioFMs). You will pioneer novel deep learning architectures and pre-training paradigms that learn the fundamental language of the genome and cellular biology. Rather than just applying out-of-the-box ML to biological datasets, you will design the next generation of BioFMs from tackling complex -omics data at scale. If you are a first-principles thinker excited to bridge advanced ML with genome biology to solve high-impact, frontier problems in human health and drug discovery, this is a unique opportunity.\n\n\n","salary_min":175000,"salary_max":200000,"location":"Toronto, Canada","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","pre-training","generative-ai","machine-learning","research"],"apply_url":"https://jobs.lever.co/deepgenomics/74978439-123f-4000-ae76-e70730c6cfa0/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-08T20:39:58.761Z","expires_at":"2026-08-14T14:13:45.130369Z","created_at":"2026-06-28T14:11:15.343855Z","updated_at":"2026-07-15T14:13:45.252634Z","company_name":"Deep Genomics","company_slug":"deep-genomics","company_logo_url":"https://www.google.com/s2/favicons?domain=deepgenomics.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4a0ffe7d-807a-4b2f-adf3-b421a54fbf3a"},{"id":"2b451cf3-6c71-4104-8ff0-5279bfb96394","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Tech Lead Manager, Foundation Models","slug":"tech-lead-manager-foundation-models-792e6190","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 mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. \n This role follows a hybrid work schedule and reports to a Research Director . \n  \n You will: \n \n Lead and manage a team to build large-scale foundation models, powering many offboard and onboard applications for the Waymo Driver\n Set the technical direction for the team, develop the execution strategies, and align individual efforts with the overall strategies\n Develop a collaborative relationship with cross-functional teams across Waymo, including simulation, planner, semantics, and ML infrastructure\n Establish an inclusive culture for the team, enabling research innovations and delivering impactful results\n \n  \n You have: \n \n 2+ years of experience managing medium-size teams\n Track record in developing team culture and growing people\n 6+ years experience in deep learning research or applied research\n Technical experience working with LLMs / VLMs or world models\n \n  \n We prefer: \n \n Strong background in foundation model  related domains, such as pretraining, mid-training, post-training, Transformer modeling, model scaling, self-supervised learning.\n Experience working with productionizing deep learning models\n \n  \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:\n \n Health, dental, vision, life, disability insurance \n Retirement Benefits: 401(k) with company match \n Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment \n Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) \n Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks \n Baby Bonding Leave: 18 weeks \n Holidays: 13 paid days per year \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 $298,000 — $368,000 USD","salary_min":298000,"salary_max":368000,"location":"Mountain View, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","llm","pre-training","autonomous-vehicles","reinforcement-learning","generative-ai"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7974331","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-01T22:17:46Z","expires_at":"2026-08-14T14:06:32.786785Z","created_at":"2026-06-28T14:04:32.312372Z","updated_at":"2026-07-15T14:06:32.912452Z","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/2b451cf3-6c71-4104-8ff0-5279bfb96394"},{"id":"4ab2e3ca-532b-47f4-9957-6020b204eaaf","company_id":"d49c7f16-1314-459a-acab-7b3d38ee01a9","title":"Member of Technical Staff, Evals","slug":"member-of-technical-staff-evals-74e01b55","description":"Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.\n\n\n\n\nABOUT THE ROLE\n\nEvals builds the internal platform that teams across Magic use to evaluate the performance of internal and external models. The team supports pre-training, post-training, data, inference, and product, and sits on the critical path of many of the company's most important decisions.\n\nAs a Member of Technical Staff on Evals, you will build both the platform and the evaluations themselves. You'll develop infrastructure for large-scale evaluations, data ablations, and dataset quality analysis, while designing and validating the methodologies used to measure model performance.\n\nSweating the details matters on this team. Many benchmarks, papers, and open-source evaluation frameworks contain subtle bugs or flawed assumptions that lead to misleading conclusions. We care deeply about correctness, reproducibility, and measurement quality.\n\nEvals are essential to the success of the company. By building trustworthy evaluation systems, you will help Magic make better research decisions, build better datasets, and ship better products.\n\n\n\n\nWHAT YOU'LL WORK ON\n\n - Build and maintain the internal evals platform used across Magic\n\n - Design, implement, and validate eval tasks for pre-training, post-training, reinforcement learning, inference, and product systems\n\n - Develop infrastructure for running large-scale evaluations\n\n - Build systems to measure dataset quality and identify opportunities to improve training data\n\n - Improve evaluation correctness, reproducibility, and reliability\n\n - Audit and improve upon public benchmarks, evaluation methodologies, and open-source implementations\n\n - Partner with research, data, inference, and product teams to define metrics that accurately reflect model quality\n\n - Build tooling and frameworks that enable teams across Magic to make decisions based on trustworthy measurements\n\n\nWHAT WE'RE LOOKING FOR\n\n - Experience building production systems, internal platforms, or developer infrastructure\n\n - Experience working with machine learning systems, evaluation frameworks, data infrastructure, or research tooling\n\n - Track record of owning technical projects end-to-end\n\n - Skepticism toward results that cannot be reproduced, validated, or explained\n\n - Ability to reason critically about benchmarks, metrics, and experimental methodology\n\n - Experience designing, implementing, or operating systems that run at scale\n\n - Comfortable navigating ambiguity and determining whether a measurement is actually capturing the behavior it claims to measure\n\n - Excitement about helping researchers and engineers make better decisions through trustworthy measurements\n\n\nCOMPENSATION, BENEFITS, AND PERKS (US)\n\n - Annual salary range between $200K - $550K depending on experience\n\n - Equity is a significant part of total compensation, in addition to salary\n\n - 401(k) plan with 6% salary matching\n\n - Generous health, dental, and vision insurance for you and your dependents\n\n - Unlimited paid time off\n\n - Visa sponsorship and relocation support for candidates moving to San Francisco\n\n - A small, fast-moving, highly collaborative team working on frontier AI systems\n\nMagic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.\n\n\n\n\nOUR CULTURE\n\n - Integrity. Words and actions should be aligned\n\n - Hands-on. At Magic, everyone is building\n\n - Teamwork. We move as one team, not N individuals\n\n - Focus. Safely deploy AGI. Everything else is noise\n\n - Quality. Magic should feel like magic","salary_min":200000,"salary_max":550000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","pre-training","code-generation","evaluation"],"apply_url":"https://jobs.ashbyhq.com/magic.dev/49e62c0f-ee70-4c6d-95dc-1ac4132ca5cf/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-01T21:37:20.708Z","expires_at":"2026-08-14T14:07:02.839987Z","created_at":"2026-06-28T14:05:01.254114Z","updated_at":"2026-07-15T14:07:02.963775Z","company_name":"Magic","company_slug":"magic","company_logo_url":"https://www.google.com/s2/favicons?domain=magic.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4ab2e3ca-532b-47f4-9957-6020b204eaaf"},{"id":"1e411b32-416c-4234-bcb3-3604b204f141","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Staff Machine Learning Engineer, AV Core","slug":"staff-machine-learning-engineer-av-core-1f2ae697","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The role  \n As a Staff Machine Learning Engineer on Wayve’s Core Model Safety team in AV Core, you will help shape what our end-to-end driving model must understand to be safe and reliable in the real world - and turn that into trained capabilities, clear evidence, and adoption on the shared backbone across core and product engineering.\n  \n The Core Model Safety team builds foundational capabilities for assisted and automated driving - collision avoidance, scene understanding, model understanding, and robustness under failure. You will work in a focused, high-impact senior team with strong ownership, access to large-scale training and fleet data, and close partners in research, simulation, evaluation, and applied engineering.\n  \n Key responsibilities \n \n Drive Core Model Safety roadmap themes owning the full lifecycle from research to offline/online experiments to technology transfer.\n Train and deploy end-to-end AV 2.0 models on our global fleet, using large-scale, diverse data to validate capabilities and improve generalisation across vehicles, markets, and driving conditions.\n Build high-value open-loop and closed-loop evaluations for core capabilities and representation learning.\n Align priorities and learn from the organisation - with AV Core, Evaluation, and Product Engineering on roadmaps and failure modes; from fleet, simulation, and product feedback; and through mentoring others on the team.\n Maintain awareness of the wider business context - division and company priorities, near-term product programmes, and how Core Model Safety work enables them.\n \n About you   \n In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n  \n Essential  \n \n 5+ years in ML engineering, including pathfinding in ambiguous problems - from scoping and evals to establishing a direction (and knowledge transfer) for others to build on.\n Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.\n Hands-on experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent.\n Hands-on experience training shared representations with multiple tasks or objectives (multi-stage or joint training), including real trade-offs across data and losses.\n Staff-level technical leadership: research-literate and pragmatic, setting direction, raising the bar, and leading cross-functional work without formal line management.\n \n  \n Desirable  \n \n Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems.\n Experience in 3D scene understanding and representation learning for geometric and semantic perception, large-scale semantic enrichments.\n Experience in reward modelling, behaviour modelling, model introspection, and/or interpretability.\n Experience with redundant or fallback architectures, safety-critical systems.\n Experience across foundations/pretraining and applied engineering teams; large-scale training infrastructure and/or agentic workflows.\n \n This is a full-time role based in our office in Sunnyvale.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. The reasonably estimated salary for this role ranges from $336,400 to $370,300, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.\n Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. \n We understand that everyone has a unique set of skills and experiences and that no","salary_min":336400,"salary_max":370300,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pre-training","agents","reinforcement-learning","autonomous-vehicles","pytorch","gpu","generative-ai","robotics"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8562545002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T19:02:03Z","expires_at":"2026-08-14T14:15:22.91234Z","created_at":"2026-05-27T14:13:12.451192Z","updated_at":"2026-07-15T14:15:23.051417Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1e411b32-416c-4234-bcb3-3604b204f141"},{"id":"805ed157-8bdf-4b5b-91cb-a8a94d8c0226","company_id":"a0000000-0000-0000-0000-000000000003","title":"Technical Lead Manager, Physical AI","slug":"technical-lead-manager-physical-ai-13b9c024","description":"Scale AI is the data engine for the entire AI industry. Our mission is to accelerate the development of AI applications by providing organizations with the high-quality data they need. The Physical AI team at Scale is focused on the next frontier: building general AI that can reason and act in the physical world. By leveraging Scale’s massive data infrastructure, we are helping frontier labs build Foundation Models for Physical AI that will redefine the future of automation.\n Role Overview \n As the Technical Lead Manager (TLM) for the Physical AI team of Scale , you will bridge the gap between cutting-edge Machine Learning research and physical robot deployment. You will lead a high-performing team of Research Engineers while remaining a hands-on technical contributor (~60% of your time).\n Your primary focus will be the development and evaluation of Large-Scale Foundation Models (e.g VLAs, World models) that allow robots and AVs to generalize across diverse tasks, environments, and morphologies.\n Key Responsibilities \n Technical Leadership \u0026 Research \n \n Model Scaling: Direct research into scaling laws for Physical AI, determining how to best utilize massive datasets for pre-training and fine-tuning generalist policies.\n VLA and World model development: Develop novel methods for developing and evaluating models, including new Physical AI industry benchmarks\n Hands-on Modeling: Actively write code to implement, train and test SOTA architectures. Conduct research on Physical AI data collection, cross-embodiment training, and policy fine-tuning.  \n Data Strategy: Collaborate with internal labeling teams to design \"robotic-native\" data pipelines, including the use of VLMs for automated trajectory annotation and data synthesis.\n Collaborate closely with customers to drive the industry forward in using Scale data \n \n Team Management \u0026 Execution \n \n Mentorship: Lead and grow a team of 4-6 elite Physical AI  researchers, fostering a culture of high-velocity experimentation and rigorous evaluation.\n Paper-to-Product: Translate the latest research from NeurIPS, ICRA, and CVPR into production-ready features for Scale’s Physical AI partners.\n Cross-functional Alignment: Work with cross-functional teams (e.g Product and Operations) to bring our research breakthroughs into production. \n \n Required Qualifications \n AI/ML Excellence \n \n Deep Learning Mastery: Expert-level proficiency in PyTorch , with deep knowledge of Transformer architectures , Attention mechanisms , and Self-Supervised Learning .\n VLM/VLA Experience: Proven track record of working with Vision-Language Models (e.g., CLIP, PaLM-E) and adapting them for spatial reasoning or embodied tasks.\n Generative AI: Experience with Diffusion Models for sequence generation or Generative World Models for predictive modeling.\n \n Physical AI \u0026 Software Background \n \n Embodied AI: Strong understanding of Physical AI stack, including imitation learning, reinforcement learning (RL), and multi-modal sensor fusion.\n Infrastructure: Experience with large-scale distributed training across GPU clusters and high-performance data loading.\n Leadership: 1+ years of experience leading technical teams or projects in a research-intensive environment.\n \n Nice to Haves: \n \n Publication Record: First-author publications at top-tier AI/ML conferences (NeurIPS, CVPR, ICRA, CoRL).\n Hardware Generalization: Experience building models that work across different robot types (arms, mobile bases, humanoids).\n Sim-to-Real: Experience with high-fidelity simulators (e.g., Isaac Gym, MuJoCo) and the nuances of physical domain adaptation.\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 The base salary range for this full-time position in the location of San Francisco is:\n $248,800 — $311,000 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n ","salary_min":248800,"salary_max":311000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","pre-training","cloud","gpu","generative-ai","robotics","diffusion-models","search"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4693453005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-08T16:16:36Z","expires_at":"2026-08-14T14:01:52.273472Z","created_at":"2026-05-10T14:01:23.558094Z","updated_at":"2026-07-15T14:01:52.398636Z","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/805ed157-8bdf-4b5b-91cb-a8a94d8c0226"},{"id":"6cc4f021-a9ac-47ce-8f68-956479b4a3e0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Operations, External Artifacts","slug":"research-operations-external-artifacts-f2170026","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 Anthropic publishes risk reports: long-form technical documents laying out our assessment of the most serious potential risks from our models in domains like CBRN, cyber operations, and AI autonomy, along with the evaluation results behind that assessment, the safeguards we've applied, and our reasoning for why a given model is safe to deploy under our Responsible Scaling Policy. Some risk reports are standalone periodic assessments; others are more targeted, produced when we release a specific frontier model. These are some of the most consequential documents we produce, and one of the main ways we hold ourselves publicly accountable for the safety claims we make.\n We're hiring a Research Operations Specialist to own risk report operations. You'll be embedded with safety and research teams through each report cycle: coordinating contributions from dozens of researchers, holding the schedule and the open-threads list, and making sure the document ships on time as a single, internally consistent whole. You'll also do substantive editorial work, turning evaluation results, threat models, and researcher notes into clear prose and pushing back when a safety argument doesn't hold together.\n Risk reports sit within a wider family of external safety artifacts, including system cards and Responsible Scaling Policy updates. Part of this role is keeping those documents consistent with each other so that what we commit to in one place matches what we commit to and deliver on everywhere else.\n This role sits in Research Operations and works closely with our Frontier Red Team, Safeguards, Alignment, and capabilities researchers. The job is part project management, part translation: keeping a complex, many-author, hard-deadline document on track while making frontier risk assessment legible to researchers, policymakers, journalists, and the public without losing precision.\n Key responsibilities \n \n Drive risk report production end to end: own the timeline, the contributor list, and the open-threads tracker\n Coordinate core contributors across Frontier Red Team, Safeguards, Alignment, Interpretability, and capabilities research; chase drafts, resolve disagreements, find ground truth, and run the final polish pass\n Edit (and sometimes write) content; work with researchers and red-teamers to turn evaluation results, threat models, and plots into clear, non-marketing prose, and keep Anthropic's voice consistent across sections drafted by many different people\n Guard accuracy and consistency: catch terminology drift, risk claims that subtly contradict each other, and gaps between internal findings and what the draft says\n Keep the risk report aligned with system cards, RSP disclosures, and other safety documentation, and flag conflicts early\n Improve the process between reports; build templates, style guidance, and contributor checklists so each cycle starts from a stronger baseline\n Pick up other research-adjacent operations and writing work related to our external artifacts and Anthropic's RSP\n \n Minimum qualifications \n \n Demonstrated technical writing ability: can take dense, jargon-heavy source material and produce prose that is precise and readable by a smart non-specialist\n Working conceptual knowledge of large language models, with fluency in terms like pretraining, RLHF, context windows, evals, red-teaming, and capability thresholds\n Ability to read evaluation results tables, ask clarifying questions, and identify gaps in a technical argument\n Track record of driving complex, multi-contributor projects to completion against hard deadlines\n \n Preferred qualifications \n \n Strong project coordination instincts; experience managing many parallel open threads across contributors who are juggling other high-priority work\n Ability to coordinate and influence without direct authority across research and engineering teams\n An eye for data presentation; can assess whether a chart or table could be clearer or more accurate\n Familiarity with AI safety, AI policy, alignment research, national security operations and/or policy, or threat modeling beyond baseline LLM knowledge\n Experience with safety or compliance documentation: safety cases, risk assessments, security disclosures, or clinical/scientific reporting\n Background in science communication, research publishing, or technical journalism\n Track record of shipping long-form technical documents (research reports, whitepapers, standards, or regulatory filings)\n Experience producing polished, visually consistent documents; an eye for layout and on-brand presentation\n Comfort using frontier LLM tools as a productivity aid wit","salary_min":260000,"salary_max":310000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["healthcare","reinforcement-learning","alignment","llm","pre-training","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5208278008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-05T17:50:48Z","expires_at":"2026-08-14T14:00:30.509282Z","created_at":"2026-05-06T14:00:32.514787Z","updated_at":"2026-07-15T14:00:30.640895Z","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/6cc4f021-a9ac-47ce-8f68-956479b4a3e0"},{"id":"ded9ce3f-76f4-4c51-8018-38d4f97c474a","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Machine Learning (RL Velocity)","slug":"research-engineer-machine-learning-rl-velocity-c61986b1","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 The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run.\n Responsibilities \n \n Build and improve the RL training infrastructure that researchers depend on day-to-day\n Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed\n Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster\n Own the reliability and performance of research runs end-to-end\n Contribute to design decisions that shape how Anthropic does RL at scale\n \n You may be a good fit if you \n \n Have strong software engineering fundamentals and a track record of building performant, reliable systems\n Have worked on ML infrastructure, distributed systems, or research tooling\n Care about enabling other people's work and find leverage through platforms rather than individual experiments\n Are comfortable operating across the stack, from low-level performance work to RL algorithms\n Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego\n \n Strong candidates may also have \n \n Experience with large-scale distributed training (RL, pre-training, or post-training)\n Familiarity with JAX, PyTorch, or similar ML frameworks\n A track record of operating at the edge of research and infra in a fast-moving environment\n \n Deadline to apply: None. Applications will be reviewed on a rolling basis.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $500,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe 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 communication, don't click any links—visit  anthropic.com/careers  directly for confirmed position openings.\n How we're different \n We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzz","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["pre-training","distributed-systems","alignment","pytorch","search","machine-learning","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5198108008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-23T14:35:36Z","expires_at":"2026-08-14T14:00:28.563188Z","created_at":"2026-04-30T05:46:34.053245Z","updated_at":"2026-07-15T14:00:28.685843Z","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/ded9ce3f-76f4-4c51-8018-38d4f97c474a"},{"id":"e166cabe-5ba5-4fe5-a30d-688ddd5f8fc1","company_id":"5dfcd8fc-f8dd-4f46-b613-ca6da467ff4b","title":"Machine Learning Researcher, Audio","slug":"machine-learning-researcher-audio-6b0906fa","description":"MACHINE LEARNING RESEARCHER, AUDIO\n\nLocation: San Francisco, CA or Remote\n\n \n \n\n\nABOUT BLAND\n\nAt Bland.com, our mission is to empower enterprises to build AI phone agents at scale. Based in San Francisco, we are a fast-growing team reimagining how customers interact with businesses through voice. We have raised $65 million from leading Silicon Valley investors, including Emergence Capital, Scale Venture Partners, Y Combinator, and founders of Twilio, Affirm, and ElevenLabs.\n\n \n\nVoice is quickly becoming the primary interface between businesses and their customers. We are building the models and infrastructure that make those interactions feel natural, reliable, and genuinely human.\n\n \n \n\n\nTHE ROLE: MACHINE LEARNING RESEARCHER, AUDIO\n\nAs a Machine Learning Researcher at Bland, you'll be working on foundational research and development across the core components of our voice stack: speech-to-text, large language models, neural audio codecs, and text-to-speech. Your work will define how our agents understand, reason, and speak in real time at enterprise scale.\n\n \n\nThis is not a narrow research role. You will take ideas from theory to large-scale training to production inference systems serving millions of calls per day. You will design new modeling approaches, validate them with rigorous experimentation, and collaborate with engineering teams to deploy them into real customer environments.\n\n \n \n\n\nWHAT YOU WILL DO\n\nBuild and Scale Next-Generation TTS Systems\n\n - Design and train large scale text-to-speech models capable of expressive, controllable, human-sounding output.\n\n - Develop neural audio codec-based TTS architectures for efficient, high-fidelity generation.\n\n - Improve prosody modeling, question inflection, emotional expression, and multi-speaker robustness.\n\n - Optimize for real-time, low-latency inference in production.\n\n \n\nAdvance Speech-to-Text Modeling\n\n - Build and fine-tune large scale ASR systems robust to accents, noise, telephony artifacts, and code switching.\n\n - Leverage self-supervised pretraining and large-scale weak supervision.\n\n - Improve transcription accuracy for real-world enterprise scenarios, including structured extraction and conversational nuance.\n\n \n\nPioneer Neural Audio Codecs\n\n - Research and implement neural audio codecs that achieve extreme compression with minimal perceptual loss.\n\n - Explore discrete and continuous latent representations for scalable speech modeling.\n\n - Design codec architectures that enable downstream generative modeling and controllable synthesis.\n\n \n\nDevelop Scalable Training Pipelines\n\n - Curate and process massive audio datasets across languages, speakers, and environments.\n\n - Design staged training curricula and data filtering strategies.\n\n - Scale training across distributed GPU clusters focusing on cost, throughput, and reliability.\n\n \n\nRun Rigorous Experiments\n\n - Design ablation studies that isolate the impact of architectural changes.\n\n - Measure improvements using both objective metrics and perceptual evaluations.\n\n - Validate ideas quickly through focused experiments that confirm or eliminate hypotheses.\n\n \n \n\n\nWHAT MAKES YOU A GREAT FIT\n\nDeep Research Foundations\n\n - Experience with self-supervised learning, multimodal modeling, or generative modeling.\n\n - Ability to derive new formulations and implement them efficiently.\n\n \n\nExpertise in Voice Modeling\n\n - Hands-on experience building or scaling TTS, STT, or neural audio codec systems.\n\n - Familiarity with large scale speech datasets and real-world audio variability.\n\n - Strong intuition for audio quality, prosody, and conversational dynamics.\n\n \n\nSystems and Hardware Awareness\n\n - Experience training and serving large models on modern accelerators.\n\n - Knowledge of inference optimization techniques, including quantization, kernel optimization, and memory efficiency.\n\n - Understanding of real-time constraints in telephony or streaming environments.\n\n \n\nExperimental Rigor\n\n - Track record of designing controlled experiments and meaningful ablations.\n\n - Comfortable working with both offline benchmarks and live production metrics.\n\n - Ability to move quickly from hypothesis to validation.\n\n \n\nBuilder Mentality\n\n - Comfortable in fast-moving startup environments.\n\n - Strong ownership mindset from research through deployment.\n\n - Excited by ambiguous, unsolved problems.\n\n \n \n\n\nHOW YOU SHOW UP\n\n - You treat unsolved problems as opportunities to invent new paradigms.\n\n - You identify the single experiment that can validate an idea in days, not months.\n\n - You measure everything and let data drive decisions.\n\n - You are obsessed with making voice agents sound truly human.\n\n - You use AI tools aggressively to amplify your own impact and accelerate research cycles.\n\n \n \n\n\nBONUS POINTS\n\n - Experience with large scale distributed training.\n\n - Research publications or open source contributions in speech or language AI.\n\n - Background in real-time speech systems or telephony.\n\n ","salary_min":160000,"salary_max":250000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pre-training","distributed-systems","healthcare","speech","llm","gpu","machine-learning","research"],"apply_url":"https://jobs.ashbyhq.com/bland/2e815d0d-8e7a-43cc-8853-c1b029aeb499/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-20T22:07:11.702Z","expires_at":"2026-08-14T14:08:20.678982Z","created_at":"2026-04-22T15:40:14.708917Z","updated_at":"2026-07-15T14:08:20.806669Z","company_name":"Bland AI","company_slug":"bland-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=bland.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e166cabe-5ba5-4fe5-a30d-688ddd5f8fc1"},{"id":"0dcc2e8e-a8cd-449a-9eae-c3c6916b5b85","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Multimodal Understanding","slug":"member-of-technical-staff-multimodal-understanding-d5a4390f","description":"SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.  Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \n ABOUT THE ROLE: \n You will join the multimodal team to push toward superhuman multimodal intelligence. Advance understanding and generation across modalities—image, video, audio, and text—spanning the full stack: data curation/acquisition, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences.\n Collaborate cross-functionally with pre-training, post-training, reasoning, data, applied, and product teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration. Contribute to building models that can see, hear, reason about, and interact with the world in real time at unprecedented levels.\n RESPONSIBILITIES: \n \n Design, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale.\n Develop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text).\n Advance multimodal capabilities including spatial-temporal compression, cross-modal alignment, world modeling, reasoning, emergent abilities, audio/image/video understanding \u0026 generation, real-time video processing, and noisy data handling.\n Drive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models.\n Create evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy.\n Innovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance.\n Build research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback.\n Work across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions.\n \n BASIC QUALIFICATIONS: \n \n Hands-on experience with multimodal pre-training, post-training, or fine-tuning (vision, audio, video, or cross-modal).\n Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA.\n Proven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design).\n Deep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data.\n Strong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors).\n Proactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers.\n Willingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences.\n \n PREFERRED SKILLS AND EXPERIENCE:\n \n Experience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling.\n Familiarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems.\n Proficiency in Rust and/or C++ for performance-critical components.\n Hands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes.\n Background building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership.\n Passion for end-to-end user experience in interactive, real-time multimodal AI systems.\n \n COMPENSATION AND BENEFITS: \n $180,000 - $440,000 USD\n Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n SpaceXAI is an equal opportunity employer. For details on data processing, view our R","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","agents","pytorch","jax","pre-training","llm","reinforcement-learning","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5111374007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T18:05:41Z","expires_at":"2026-08-14T14:04:43.995682Z","created_at":"2026-04-17T19:30:23.022034Z","updated_at":"2026-07-15T14:04:44.13561Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0dcc2e8e-a8cd-449a-9eae-c3c6916b5b85"},{"id":"92e0e6b0-3459-44ec-9e1a-4e36a7b805d4","company_id":"4d985fa4-b897-4f93-9745-c332367ad86b","title":"Research Scientist - LLM ","slug":"research-scientist-llm-80f40837","description":"ABOUT RETELL AI\n\nRetell AI is using first principles to reimagine the call center with cutting-edge voice AI.\n\nThousands of companies now utilize Retell’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we have scaled to $60M ARR with a team of 40 people, up from $5M at the start of 2025.\n\nOur vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. Instead of basic automation that needs constant human tuning, we’re creating intelligent AI “workers” that can act as frontline agents, QA analysts, and managers, continuously executing, monitoring, and improving customer interactions.\n\nWe’re growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact at one of the fastest-growing voice AI startups.\n\nLet’s build the future together.\n\n - We’re a top 50 AI app in a16z list: https://tinyurl.com/5853dt2x\n\n - #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025\n\n - We're also one of the top ranking startups on: https://leanaileaderboard.com/\n\n - Enterprise tech 30: https://www.wing.vc/et30/overview\n\n\n\n\nABOUT THE ROLE\n\nThis is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI. As a Founding Machine Learning Research Engineer at Retell, you’ll focus on advancing model capabilities for human-like voice agents operating in complex, real-world environments.\n\nYou’ll explore new approaches across LLMs and audio models, design novel evaluation methods, and prototype systems that improve reasoning, latency, and conversational quality. Your work will directly influence production systems, bridging cutting-edge research with real-world deployment.\n\nIf you’re excited about solving open-ended ML problems, experimenting rapidly, and shaping how voice AI systems think and perform, this is a unique opportunity to do so at scale.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Research \u0026 Experimentation – Explore and develop new techniques across LLMs and audio models to improve reasoning, latency, and conversational quality in real-time systems.\n\n - Model Training – Rapidly build and iterate on models and pipelines, turning research ideas into working prototypes. Innovate on paradigms, training methods, and inference.\n\n - Evaluation \u0026 Benchmarking – Design novel evaluation frameworks, datasets, and metrics to measure performance on complex, real-world voice tasks.\n\n - Bridge Research to Production – Collaborate closely with engineering to translate research insights into deployable systems.\n\n - Human Feedback Loops – Develop methods to incorporate human evaluation into model improvement, especially for subjective conversational quality.\n\n - Advance the Frontier – Stay at the cutting edge of ML research and bring new ideas into Retell’s product and infrastructure.\n\n\n\n\nREQUIRED\n\n - Strong ML Research Background – You've worked on advanced ML problems (like LLM pre-training and post-training, transcription model training, TTS, or multimodal systems), either in industry or academia.\n\n - Deep Technical Foundation – Comfortable with PyTorch, model architectures, and the math behind modern machine learning.\n\n - Top Academic Background – Master's degree in CS, ML, AI or related field required; PhD preferred. Equivalent research-level engineering experience also considered.\n\n\n\n\nYOU MIGHT THRIVE IF YOU\n\n - Published or Awarded – First/co-author publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, etc.) or notable competition awards are a strong plus.\n\n - Experimental Mindset – You enjoy exploring open-ended problems and iterating quickly on ideas.\n\n - Bridge Theory \u0026 Practice – You can translate research into systems that work in real-world environments.\n\n - Startup-Ready – You thrive in fast-paced environments with high ownership and ambiguity.\n\n - Collaborative \u0026 Clear Communicator – You can explain complex ideas and work cross-functionally to drive impact.\n\n\n\n\nJOB DETAILS\n\n - Cash: $225,000 - $400,000 base salary\n\n - Equity: Offers Equity\n\n - Location: Redwood City, CA, US (100% Relocation Provided)\n\n - US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.\n\n\n\n\nOTHER BENEFITS\n\n - 100% coverage for medical, dental, and vision insurance\n\n - $70/day DoorDash credit for unlimited meals and snacks\n\n - $200/month wellness reimbursement\n\n - $300/month commuter reimbursement\n\n - $75/month phone bill reimbursement\n\n - $50/month internet reimbursement\n   \n   \n\n\nCOMPENSATION PHILOSOPHY\n\n - Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed\n\n - Top 1% Talent: Above-market pay (top 5 percentile)\n\n - High Ownership: Small teams, \u003e$1M ","salary_min":225000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["speech","llm","search","pytorch","pre-training","research"],"apply_url":"https://jobs.ashbyhq.com/retell-ai/b0d780eb-df25-49d0-859a-915de204a2f2/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-14T05:52:56.477Z","expires_at":"2026-08-14T14:13:49.035862Z","created_at":"2026-04-16T11:17:45.913083Z","updated_at":"2026-07-15T14:13:49.134706Z","company_name":"Retell AI","company_slug":"retell-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=retellai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/92e0e6b0-3459-44ec-9e1a-4e36a7b805d4"},{"id":"acc5d396-6aa2-40ba-8a49-632774606bde","company_id":"4d985fa4-b897-4f93-9745-c332367ad86b","title":"Research Scientist - Audio ","slug":"research-scientist-audio-918408c6","description":"ABOUT RETELL AI\n\nRetell AI is using first principles to reimagine the call center with cutting-edge voice AI.\n\nThousands of companies now utilize Retell’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we have scaled to $60M ARR with a team of 40 people, up from $5M at the start of 2025.\n\nOur vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. Instead of basic automation that needs constant human tuning, we’re creating intelligent AI “workers” that can act as frontline agents, QA analysts, and managers, continuously executing, monitoring, and improving customer interactions.\n\nWe’re growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact at one of the fastest-growing voice AI startups.\n\nLet’s build the future together.\n\n - We’re a top 50 AI app in a16z list: https://tinyurl.com/5853dt2x\n\n - #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025\n\n - We're also one of the top ranking startups on: https://leanaileaderboard.com/\n\n - Enterprise tech 30: https://www.wing.vc/et30/overview\n\n\n\n\nABOUT THE ROLE\n\nThis is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI. As a Founding Machine Learning Research Engineer at Retell, you’ll focus on advancing model capabilities for human-like voice agents operating in complex, real-world environments.\n\nYou’ll explore new approaches across LLMs and audio models, design novel evaluation methods, and prototype systems that improve reasoning, latency, and conversational quality. Your work will directly influence production systems, bridging cutting-edge research with real-world deployment.\n\nIf you’re excited about solving open-ended ML problems, experimenting rapidly, and shaping how voice AI systems think and perform, this is a unique opportunity to do so at scale.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Research \u0026 Experimentation – Explore and develop new techniques across LLMs and audio models to improve reasoning, latency, and conversational quality in real-time systems.\n\n - Model Training – Rapidly build and iterate on models and pipelines, turning research ideas into working prototypes. Innovate on paradigms, training methods, and inference.\n\n - Evaluation \u0026 Benchmarking – Design novel evaluation frameworks, datasets, and metrics to measure performance on complex, real-world voice tasks.\n\n - Bridge Research to Production – Collaborate closely with engineering to translate research insights into deployable systems.\n\n - Human Feedback Loops – Develop methods to incorporate human evaluation into model improvement, especially for subjective conversational quality.\n\n - Advance the Frontier – Stay at the cutting edge of ML research and bring new ideas into Retell’s product and infrastructure.\n\n\n\n\nREQUIRED\n\n - Strong ML Research Background – You've worked on advanced ML problems (like LLM pre-training and post-training, transcription model training, TTS, or multimodal systems), either in industry or academia.\n\n - Deep Technical Foundation – Comfortable with PyTorch, model architectures, and the math behind modern machine learning.\n\n - Top Academic Background – Master's degree in CS, ML, AI or related field required; PhD preferred. Equivalent research-level engineering experience also considered.\n\n \n\n\nYOU MIGHT THRIVE IF YOU\n\n - Published or Awarded – First/co-author publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, etc.) or notable competition awards are a strong plus.\n\n - Experimental Mindset – You enjoy exploring open-ended problems and iterating quickly on ideas.\n\n - Bridge Theory \u0026 Practice – You can translate research into systems that work in real-world environments.\n\n - Startup-Ready – You thrive in fast-paced environments with high ownership and ambiguity.\n\n - Collaborative \u0026 Clear Communicator – You can explain complex ideas and work cross-functionally to drive impact.\n\n\n\n\nJOB DETAILS\n\n - Cash: $225,000 - $400,000 base salary\n\n - Equity: Offers Equity\n\n - Location: Redwood City, CA, US (100% Relocation Provided)\n\n - US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT).\n\n\n\n\nOTHER BENEFITS\n\n - 100% coverage for medical, dental, and vision insurance\n\n - $70/day DoorDash credit for unlimited meals and snacks\n\n - $200/month wellness reimbursement\n\n - $300/month commuter reimbursement\n\n - $75/month phone bill reimbursement\n\n - $50/month internet reimbursement\n   \n   \n\n\nCOMPENSATION PHILOSOPHY\n\n - Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed\n\n - Top 1% Talent: Above-market pay (top 5 percentile)\n\n - High Ownership: Small teams, \u003e$1M revenue/emplo","salary_min":225000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["search","pre-training","speech","llm","pytorch","research"],"apply_url":"https://jobs.ashbyhq.com/retell-ai/7dbe5404-e08c-4c62-99dc-ef050534d029/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-14T05:52:52.3Z","expires_at":"2026-08-14T14:13:48.734791Z","created_at":"2026-04-16T11:17:45.838238Z","updated_at":"2026-07-15T14:13:48.913647Z","company_name":"Retell AI","company_slug":"retell-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=retellai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/acc5d396-6aa2-40ba-8a49-632774606bde"}],"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":145,"total_pages":8}