{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL (Reinforcement Learning) ","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["gpu","distributed-systems","code-generation","search","fine-tuning","alignment","pytorch","llm"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-08-19T14:00:28.677048Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-07-20T14:00:28.765875Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"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":["pre-training","speech","reinforcement-learning","fine-tuning","distributed-systems","pytorch"],"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":"2026-08-19T14:03:42.672406Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-07-20T14:03:42.771329Z","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":["llm","agents","reinforcement-learning","pre-training","search","alignment","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":"2026-08-19T14:00:30.306999Z","created_at":"2026-04-13T09:36:01.625992Z","updated_at":"2026-07-20T14:00:30.40071Z","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":"19e591f5-9c13-4350-a7f2-7b11bbb309d9","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"Applied Scientist","slug":"applied-scientist-8dbebfe9","description":"About Dialpad Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage. \n Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved. \n Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile. \n Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. \n We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. \n We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . \n Your role As an Applied Scientist at Dialpad, you'll be an integral part of our AI team, conducting R\u0026D to power the next generation of autonomous voice agents and delivering features for transcribed voice and chat message data in the business communications domain. We have several research themes, including developing multi-modal, real-time agentic systems that can listen, reason, and take action during live customer interactions. We are also developing real-time knowledge retrieval models to power live coaching features for customer support and sales agents. Beyond the technical skills, we are a team that values collaboration, continuous learning, and the application of diverse perspectives to solve complex problems. Collaboration will be key as you work alongside our engineering, design, and product teams to build groundbreaking applications. \n If you're passionate about language, AI, and contributing to a team that's changing the face of business communications, you'll find yourself right at home with us. \n This position reports to the Manager of the NLP team and has the opportunity to be based in our Kitchener, ON, office. \n What you’ll do \n \n Develop, implement, and refine state-of-the-art Natural Language Processing and Machine Learning algorithms for Dialpad's products. \n Conduct rigorous evaluation and monitoring of model performances and troubleshoot issues with a keen understanding of the resultant business impacts. \n Manage massive textual data sets. \n Build advanced LLM-based features, including reasoning, multilingual and multimodal processing, and agents. \n Collaborate with cross-functional teams, including engineering, product, and design, to effectively deploy and scale models and algorithms in production. \n Submit papers to top-tier academic conferences and journals and contribute to the broader scientific community by reviewing submissions. \n \n Skills you’ll bring \n \n Master’s or PhD degree in Linguistics, Computational Linguistics, Computer Science, Machine Learning, or related fields. \n 2+ years of NLP industry experience for Master’s degree holders or 1+ years for PhD degree holders. \n Demonstrated experience with machine learning, Python, PyTorch, and other relevant tools and technologies. \n A broad understanding of current LLM model architectures and techniques for tuning and optimizing LLMs. \n Strong problem-solving and analytical abilities, with the capacity to handle complex technical and analytical problems. \n Excellent communication and collaboration skills to effectively work in a multi-disciplinary team.  \n Familiarity with version control tools like Git for collaborative projects. \n For exceptional talent based in British Columbia, Canada  the target base salary range for this position is posted below. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the target range for new hire salaries for the position. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in British Colu","salary_min":161500,"salary_max":191500,"location":"Vancouver, Canada","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["fine-tuning","pytorch","nlp","agents","llm","rag"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8639037002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-17T19:58:28Z","expires_at":"2026-08-19T14:32:35.587729Z","created_at":"2026-07-18T14:21:30.140698Z","updated_at":"2026-07-20T14:32:35.726095Z","company_name":"Dialpad","company_slug":"dialpad","company_logo_url":"https://www.google.com/s2/favicons?domain=dialpad.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/19e591f5-9c13-4350-a7f2-7b11bbb309d9"},{"id":"4d1bea88-b137-407e-b36f-8b5e15e4ca17","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"Applied Scientist","slug":"applied-scientist-f2739401","description":"About Dialpad Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage. \n Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved. \n Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile. \n Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. \n We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. \n We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . \n Your role As an Applied Scientist at Dialpad, you'll be an integral part of our AI team, conducting R\u0026D to power the next generation of autonomous voice agents and delivering features for transcribed voice and chat message data in the business communications domain. We have several research themes, including developing multi-modal, real-time agentic systems that can listen, reason, and take action during live customer interactions. We are also developing real-time knowledge retrieval models to power live coaching features for customer support and sales agents. Beyond the technical skills, we are a team that values collaboration, continuous learning, and the application of diverse perspectives to solve complex problems. Collaboration will be key as you work alongside our engineering, design, and product teams to build groundbreaking applications. \n If you're passionate about language, AI, and contributing to a team that's changing the face of business communications, you'll find yourself right at home with us. \n This position reports to the Manager of the NLP team and has the opportunity to be based in our Kitchener, ON, office. \n What you’ll do \n \n Develop, implement, and refine state-of-the-art Natural Language Processing and Machine Learning algorithms for Dialpad's products. \n Conduct rigorous evaluation and monitoring of model performances and troubleshoot issues with a keen understanding of the resultant business impacts. \n Manage massive textual data sets. \n Build advanced LLM-based features, including reasoning, multilingual and multimodal processing, and agents. \n Collaborate with cross-functional teams, including engineering, product, and design, to effectively deploy and scale models and algorithms in production. \n Submit papers to top-tier academic conferences and journals and contribute to the broader scientific community by reviewing submissions. \n \n Skills you’ll bring \n \n Master’s or PhD degree in Linguistics, Computational Linguistics, Computer Science, Machine Learning, or related fields. \n 2+ years of NLP industry experience for Master’s degree holders or 1+ years for PhD degree holders. \n Demonstrated experience with machine learning, Python, PyTorch, and other relevant tools and technologies. \n A broad understanding of current LLM model architectures and techniques for tuning and optimizing LLMs. \n Strong problem-solving and analytical abilities, with the capacity to handle complex technical and analytical problems. \n Excellent communication and collaboration skills to effectively work in a multi-disciplinary team.  \n Familiarity with version control tools like Git for collaborative projects. \n For exceptional talent based in Ontario, Canada  the target base salary range for this position is posted below. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the target range for new hire salaries for the position. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in Ontario role postings","salary_min":145500,"salary_max":172500,"location":"Kitchener, Canada","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["nlp","llm","pytorch","fine-tuning","rag","agents"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8633549002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-17T19:58:26Z","expires_at":"2026-08-19T14:32:35.678278Z","created_at":"2026-07-18T14:21:30.22407Z","updated_at":"2026-07-20T14:32:35.796769Z","company_name":"Dialpad","company_slug":"dialpad","company_logo_url":"https://www.google.com/s2/favicons?domain=dialpad.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4d1bea88-b137-407e-b36f-8b5e15e4ca17"},{"id":"1fdeb8dc-c9f7-409e-b65e-29d86ef4a35b","company_id":"3029e985-56bf-4ac2-9ae1-df4cdd53b12f","title":"Workday Integration Developer","slug":"workday-integration-developer-5432c971","description":"About Zscaler \n Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise , we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location.\n Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate —we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession , collaboration, ownership, and accountability.\n We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity.\n Role \n We are looking for a Lead Workday Integration Developer (People Tech \u0026 AI) to join our team. This is a Remote role, reporting to the Director in the IT/Corporate Apps department. In this role, you will lead the evolution of our HR tech stack by blending deep Workday expertise with AI-driven development. You will leverage AI-assisted coding and agentic workflows to build automated solutions that solve complex People \u0026 Culture challenges and help us build seamless, intelligent employee experiences across our entire HR landscape.\n What you’ll do (Role Expectations) \n \n \n Own the full lifecycle of cross-platform People \u0026 Culture solutions, bridging systems like Workday, ServiceNow HRSD, and broader HR tooling to architect secure, scalable, and seamless employee experiences\n \n Spearhead the evaluation, design, and implementation of AI-driven HR solutions and leverage AI applications, AI-assisted coding practices, and multi-agent orchestration to accelerate development, optimize data mapping, and build robust automated workflows\n \n Serve as the senior subject matter expert and strategic partner to HR, Payroll, and IT executives, translating complex technical concepts and AI capabilities into actionable business value, ensuring optimal user experiences and cross-platform synergy\n \n Drive AI and P\u0026C technology investments by developing data-driven business cases with clear ROI, defining key success metrics for adoption, and creating compelling presentations to communicate strategy and impact to executive leadership\n \n Develop and execute comprehensive implementation plans using agile methodologies, and design robust test plans and scripts to ensure all Workday integrations and custom AI applications are delivered on time, high-quality, and bug-free\n \n Who You Are (Success Profile) \n \n \n You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.\n \n You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.\n \n You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.\n \n You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.\n \n You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.\n \n What We’re Looking for (Minimum Qualifications) \n \n \n Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within your functional domain\n \n 8+ years of software engineering or systems integration experience, including at least 4 years of proven architectural-level understanding of the Workday Integrations framework (Payroll Integrations, Core Connectors, EIBs, Workday Studio, and advanced reporting like BIRT or Custom RaaS APIs)\n \n ","salary_min":134400,"salary_max":168000,"location":"Remote (Canada)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","security","agents","llm"],"apply_url":"https://job-boards.greenhouse.io/zscaler/jobs/5190070007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-17T19:48:04Z","expires_at":"2026-08-19T14:20:50.055266Z","created_at":"2026-07-18T14:10:06.966081Z","updated_at":"2026-07-20T14:20:50.152449Z","company_name":"Zscaler","company_slug":"zscaler","company_logo_url":"https://www.google.com/s2/favicons?domain=zscaler.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1fdeb8dc-c9f7-409e-b65e-29d86ef4a35b"},{"id":"96ba0be2-0b27-42c5-bc86-c4ffbb0b4359","company_id":"238f4b8f-1e78-4053-9068-017564d76785","title":"Applied Research Engineer","slug":"applied-research-engineer-8d39811c","description":"Our Mission \u0026 Values:\nAt Drata, we help companies earn and keep the trust of their users, customers, partners, and prospects. We’re the proof layer that shows great companies deserve the trust they aim to build.\n\nWe live our values every day. Built on Trust means consistency is everything. Act with Integrity by always doing the right thing. Being Customer-Obsessed keeps the people we serve at the center of our work. Competitive Fire drives us to push ourselves harder than anyone else. Diversity brings unique perspectives that lead to better solutions. Automation First ensures we save time and money by making efficiency a priority.\n\nOur Culture \u0026 Work Style 🚀\n\nAt Drata, we’re not just building software - we’re building a mindset. Everything we do springs from:\n\n - Be a Driver (Owner‑Operator Mentality): Own your work. Improve relentlessly. Deliver results.\n\n - Move at Drata Speed (Precision \u0026 Velocity): Fast decisions. Quick learning. Immediate impact.\n\n - Stay Mission-Driven (Customer‑Obsessed): Challenge assumptions. Deliver value. Stay hungry.\n\nWe pair that high-velocity culture with a thoughtful hybrid model because we believe flexibility and collaboration both matter. That’s why in the Bay we come together in-office Tuesday through Thursday our high‑impact collaboration days where teams align, strategize, and innovate. Mondays and Fridays are flexible, giving you space for focused work, balance, and autonomy.\n\nIf you thrive when you’re empowered, energized, and working with smart, mission-driven people, you’ll feel at home here.\n\nWhy Join The Drata Team?\n\nThe best way to understand the Driver’s Mindset is to see it in action. We’re an award-winning, mission-driven team of 600+ people worldwide, united by a culture that values trust, speed, and continuous growth.\n\n - See the Speed: https://www.youtube.com/watch?v=QidTdkGwKMY Watch our CEO, Adam Markowitz, discuss the hyper-growth journey, from $0 to $100M ARR in just four years\n\n - Hear the Voice of the Team https://drata.com/about/life-at-drata: Explore our \"Life at Drata\" page for employee testimonials on our collaborative and the growth opportunities available.\n\n - Experience the Impact https://www.greatplacetowork.com/certified-company/7044563: See why we are consistently recognized on Fortune's Best Workplaces lists.\n\n - Connect with Us on Socials: LinkedIn https://www.linkedin.com/company/drata/posts/?feedView=all - follow us for company updates, employee stories, and career news.\n\nJob Summary:\n\nDrata is seeking an Applied Research Engineer to drive the quality and effectiveness of our AI systems through rigorous experimentation, evaluation, and applied research.\n\nThis is a research-focused role emphasizing experimentation and rigor over production engineering. You'll own the science behind how Drata's AI products retrieve, reason, and respond — and you'll work closely with AI and Software Engineers to turn validated approaches into production-ready systems.\n\nDrata's compliance platform is document-heavy: VRM Agent, AIQA, Trust Agent, policy-to-control mappers, and more all depend on high-quality information retrieval and reasoning.\n\nWhat you'll do:\n\n - Design and evaluate information access + reasoning strategies across RAG, agents, and classic ML: chunking, embedding models, hybrid search, metadata filtering, semantic routing\n\n - Prototype GenAI workflows (including agentic systems) that map and reason over compliance objects (controls ↔ risks ↔ requirements ↔ evidence)\n\n - Explore ML + probabilistic approaches where GenAI is not the best fit: classifiers, ranking models, graph/link prediction, calibration, and structured prediction\n\n - Build and maintain evaluation frameworks: golden datasets, automated quality metrics, regression detection\n\n - Implement and tune ranking/reranking systems: cross-encoders, LLM-based rerankers, learning-to-rank, custom scoring functions\n\n - Run experiments to validate hypotheses and quantify improvements before production rollout\n\n - Debug failure modes and build error taxonomies across retrieval, reasoning, and generation\n\n - Collaborate with AI and Software Engineers to hand off validated approaches for productionization\n\n - Stay current on applied research in RAG, agents, LLM evaluation, and relevance modeling; bring innovations into the product\n\nWhat you'll bring:\n\n - 3+ years of experience in applied research, data science, or ML with a focus on NLP, information retrieval, or knowledge systems\n\n - 1+ years of hands-on experience building or contributing to production AI/ML systems\n\n - Strong foundation in information retrieval: dense and sparse retrieval, embedding models, search relevance\n\n - Experience with RAG systems: chunking strategies, vector databases, retrieval optimization\n\n - Proficiency in evaluation methodology: metrics design, golden dataset creation, A/B testing, statistical significance\n\n - Strong Python skills and comfort with notebook-driven research workflow","salary_min":145200,"salary_max":196400,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["nlp","healthcare","generative-ai","llm","agents","rag","search","embeddings"],"apply_url":"https://jobs.ashbyhq.com/drata/51a418d1-c371-4f9f-b248-2c3b542bec42/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-17T17:00:41.883Z","expires_at":"2026-08-19T14:26:14.639324Z","created_at":"2026-05-27T14:14:33.116975Z","updated_at":"2026-07-20T14:26:14.766844Z","company_name":"Drata","company_slug":"drata","company_logo_url":"https://www.google.com/s2/favicons?domain=drata.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/96ba0be2-0b27-42c5-bc86-c4ffbb0b4359"},{"id":"4e82f55c-f0a5-45cd-bb0d-c0f71d6756d3","company_id":"238f4b8f-1e78-4053-9068-017564d76785","title":"Senior AI Engineer","slug":"senior-ai-engineer-323e04ab","description":"Our Mission \u0026 Values:\nAt Drata, we help companies earn and keep the trust of their users, customers, partners, and prospects. We’re the proof layer that shows great companies deserve the trust they aim to build.\n\nWe live our values every day. Built on Trust means consistency is everything. Act with Integrity by always doing the right thing. Being Customer-Obsessed keeps the people we serve at the center of our work. Competitive Fire drives us to push ourselves harder than anyone else. Diversity brings unique perspectives that lead to better solutions. Automation First ensures we save time and money by making efficiency a priority.\n\nOur Culture \u0026 Work Style 🚀\n\nAt Drata, we’re not just building software - we’re building a mindset. Everything we do springs from:\n\n - Be a Driver (Owner‑Operator Mentality): Own your work. Improve relentlessly. Deliver results.\n\n - Move at Drata Speed (Precision \u0026 Velocity): Fast decisions. Quick learning. Immediate impact.\n\n - Stay Mission-Driven (Customer‑Obsessed): Challenge assumptions. Deliver value. Stay hungry.\n\nWhile we’re ideally looking for someone who can join us in a hybrid capacity from our San Francisco office, we know great talent isn’t limited by zip code. We’ll consider remote candidates across the U.S. who are a strong match for the role.\n\nIf you thrive when you’re empowered, energized, and working with smart, mission-driven people, you’ll feel at home here.\n\nWhy Join The Drata Team?\n\nThe best way to understand the Driver’s Mindset is to see it in action. We’re an award-winning, mission-driven team of 600+ people worldwide, united by a culture that values trust, speed, and continuous growth.\n\n - See the Speed: https://www.youtube.com/watch?v=QidTdkGwKMY Watch our CEO, Adam Markowitz, discuss the hyper-growth journey, from $0 to $100M ARR in just four years\n\n - Hear the Voice of the Team https://drata.com/about/life-at-drata: Explore our \"Life at Drata\" page for employee testimonials on our collaborative and the growth opportunities available.\n\n - Experience the Impact https://www.greatplacetowork.com/certified-company/7044563: See why we are consistently recognized on Fortune's Best Workplaces lists.\n\n - Connect with Us on Socials: LinkedIn https://www.linkedin.com/company/drata/posts/?feedView=all - follow us for company updates, employee stories, and career news.\n\nJob Summary:\n\nDrata is advancing the frontier of compliance automation by integrating intelligent AI capabilities into its platform. We are seeking a Senior AI Engineer to help design, build, and scale robust, high-impact AI systems that improve operational efficiency, enhance decision-making, and support trust-critical enterprise workflows.\n\nThis role focuses on solving complex, real-world problems with agentic AI, LLMs, and intelligent reasoning systems, including automating questionnaire answering and other compliance-related workflows. You will work on creating generalizable AI solutions that can be applied across multiple products and domains, collaborating with cross-functional teams to bring cutting-edge research into production-ready systems.\n\nAs a Senior AI Engineer, you will own the end-to-end design, implementation, and evolution of AI-driven systems embedded directly into Drata’s core platform. Your work will focus on building generalizable, reusable AI capabilities rather than one-off features, enabling multiple product surfaces and workflows across compliance and security.\n\nWhat you'll do:\n\nBuild Agentic \u0026 Intelligent AI Systems\n\n - Design and implement LLM-powered systems capable of multi-step reasoning, evidence grounding, and decision support in high-trust domains.\n\n - Develop agentic workflows that combine retrieval, tool use, structured reasoning, and human oversight.\n\n - Create interactive AI experiences that allow users to engage naturally with complex compliance and risk data.\n\nAutomated Reasoning Over Regulations \u0026 Evidence\n\n - Build AI systems that reason over structured and unstructured data to support regulatory interpretation, control alignment, and evidence validation.\n\n - Ensure AI outputs are traceable, explainable, and auditable, meeting the expectations of enterprise customers and auditors.\n\nProduction-Grade AI Architecture\n\n - Architect and deploy scalable LLM + retrieval + agent systems in production environments.\n\n - Optimize for latency, cost, reliability, and evaluation in real-world enterprise workloads.\n\n - Partner with platform, security, product teams, and other application development teams with diverse skill sets to operationalize AI safely and effectively.\n\nResponsible \u0026 Trustworthy AI\n\n - Embed human-in-the-loop workflows, confidence thresholds, and safety guardrails into AI systems.\n\n - Ensure privacy-preserving data handling, robust failure modes, and transparent behavior aligned with Drata’s trust principles.\n\nWhat you'll bring:\n\n - Experience: 7+ years of hands-on software engineering experience; 2+ years specifically in ","salary_min":153600,"salary_max":207800,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","embeddings","agents","healthcare","rag"],"apply_url":"https://jobs.ashbyhq.com/drata/938b5802-714a-4843-bf43-4b03b3c0bf12/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-17T16:55:27.822Z","expires_at":"2026-08-19T14:26:13.494314Z","created_at":"2026-04-16T15:56:22.627903Z","updated_at":"2026-07-20T14:26:13.588071Z","company_name":"Drata","company_slug":"drata","company_logo_url":"https://www.google.com/s2/favicons?domain=drata.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4e82f55c-f0a5-45cd-bb0d-c0f71d6756d3"},{"id":"ca63bf78-8946-4739-9cb3-1fda8239777e","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior ML Scientist, Biological Systems","slug":"senior-ml-scientist-biological-systems-5083ffd9","description":"Your Impact at LILA \n Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), we develop autonomous-science capabilities for cellular and tissue biology, spanning single-cell omics, perturbation biology, spatial profiling, imaging, genetics, and multi-modal experimental data that integrate deep biological expertise with foundation modeling and agentic systems.\n We are seeking a Senior Machine Learning Scientist to help execute this vision by building autonomous life science systems grounded in epistemology, scientific methodology, Bayesian argumentation, and automation. This role will translate the scientific direction of Autonomous Life Science AI into working architectures, workflows, and evaluation methods that allow AI systems to reason rigorously about biological hypotheses, propose experiments, incorporate evidence, and accelerate discovery.\n This is a hands-on scientific and technical role for someone who can operate at the intersection of machine learning, biological reasoning, agentic systems, and experimental design. The right person will be comfortable formalizing how scientific knowledge is represented, how uncertainty is handled, how evidence changes belief, and how automated systems can execute increasingly rigorous cycles of life science discovery.\n What You'll Be Building \n \n Build autonomous life science systems that connect AI reasoning, biological evidence, experimental design, and automated execution.\n Translate the broader Autonomous Life Science AI vision into concrete architectures, workflows, prototypes, and production-quality research systems.\n Develop methods for representing hypotheses, uncertainty, evidence, and scientific arguments in ways that enable robust machine reasoning.\n Apply Bayesian reasoning, epistemology, and scientific methodology to the design of AI systems that can propose, test, and revise biological hypotheses.\n Design agentic workflows that plan experiments, reason over results, and close the loop between computational predictions and automated laboratory feedback.\n Partner with ML scientists, experimental scientists, automation teams, and platform teams to ensure systems are biologically grounded and experimentally actionable.\n Build evaluation frameworks for autonomous discovery systems, including benchmarks for reasoning quality, hypothesis generation, evidence integration, and experimental utility.\n Contribute to the technical roadmap for autonomous life science research systems and help raise the scientific rigor of the team’s approach.\n \n What You'll Need to Succeed \n \n PhD in Computer Science, Machine Learning, Computational Biology, Statistics, Biology, or a related quantitative field.\n Strong research track record in machine learning, AI for science, computational biology, probabilistic modeling, agentic systems, or a related area.\n Deep understanding of scientific reasoning, experimental design, uncertainty, and evidence integration.\n Experience building or researching systems that reason over complex scientific, biological, or experimental data.\n Strong foundation in modern ML methods, with hands-on experience in frameworks such as PyTorch, JAX, or TensorFlow.\n Ability to translate biological questions into computational and ML problems, and to translate ML system behavior back into scientific terms.\n Strong technical judgment, with the ability to operate in open-ended research settings where the right architecture, abstraction, or evaluation method is not yet obvious.\n Excellent collaboration skills across AI, biology, automation, and platform teams.\n \n Bonus Points For \n \n Experience with Bayesian modeling, probabilistic programming, causal inference, or formal methods for reasoning under uncertainty.\n Experience building agentic, active-learning, closed-loop, or autonomous-science systems.\n Familiarity with biological data modalities such as single-cell omics, perturbation data, imaging, spatial profiling, genetics, or multi-omics.\n Experience designing systems that generate, rank, test, or revise scientific hypotheses.\n Background in philosophy of science, epistemology, scientific methodology, or formal argumentation.\n Experience integrating computational predictions with experimental or automated lab workflows.\n Track record of publishing or presenting work at premier ML, computational biology, or scientific venues.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a","salary_min":268000,"salary_max":336000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","generative-ai","agents","tensorflow"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4308126009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-17T04:46:11Z","expires_at":"2026-08-19T14:29:55.952905Z","created_at":"2026-07-18T14:19:17.961775Z","updated_at":"2026-07-20T14:29:56.049302Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ca63bf78-8946-4739-9cb3-1fda8239777e"},{"id":"7a9c3c59-7703-4bde-a302-10e099cde6b9","company_id":"fb64b18b-041a-43de-886d-f506d1ab94a4","title":"Senior Staff AI Platform Engineer","slug":"senior-staff-ai-platform-engineer-093ce67a","description":"Our Purpose \n At SentinelOne, we are driven by a clear purpose: to give the advantage to those who secure our future. As AI reshapes how organizations build, operate, and innovate, the responsibility to protect them becomes more critical than ever. When you join SentinelOne, your work helps protect global enterprises, critical infrastructure, and the technologies shaping tomorrow. If you are motivated by meaningful challenges and want your impact to be real, measurable, and global, you will find purpose here.\n About Us \n SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. Our AI-native platform unifies protection across endpoint, cloud, identity, data, and AI systems to deliver autonomous detection and response with clarity and speed. By combining real-time analytics, intelligent automation, and a unified data foundation, we reduce noise, simplify complexity, and empower security teams to focus on what truly matters.\n Our teams are builders, problem-solvers, and innovators committed to shaping the future of security. If you are excited to solve hard problems alongside talented, mission-driven people, we invite you to help us build a safer future for humanity.\n What Are We Looking For? \n We’re looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes.\n As a Senior Staff AI Platform Engineer, you will be tasked with serving as a hands-on builder of SentinelOne's enterprise AI platform, including the Gateway, Harness, and Semantic Layers that every internal AI use case runs on top of. You will write the production code that governs, observes, secures, and scales AI activity across the company, working closely with the Sr. Director of Enterprise AI Platform Engineering and the rest of the platform team to take the architecture from design into running infrastructure.\n What Will You Do? \n Primary responsibilities include:\n \n Design, build, and operate components of the AI Gateway including centralized identity and authentication/authorization, token budgeting, DLP and content guardrails, multi-model routing and failover, MCP allow-listing, and request-level audit logging.\n Build and maintain pieces of the Harness layer including agent orchestration (LangGraph or equivalent), prompt construction and context management, memory and state handling across multi-turn interactions, and model abstraction across providers such as AWS Bedrock and Google Vertex.\n Develop and extend production services in Python (FastAPI) and pydantic.ai for LLM-powered components, and contribute to the React/TypeScript surfaces that expose platform capabilities to internal teams.\n Implement MCP/tool wiring for internal and SaaS-embedded agents, ensuring every caller regardless of origin passes through the same policy controls.\n Contribute to the Claude and Gemini Enterprise plugin framework by building, testing, and hardening role-based skills and agents, and help mature the pipeline for how plugins are authored, evaluated, and deployed.\n Instrument the platform for observability including metrics, tracing, and audit trails, and participate in on-call and operational support for platform services.\n Write clear technical documentation and participate in architecture and code reviews, holding a high bar for quality, security, and maintainability.\n Partner with engineers across Enterprise Data, Enterprise Apps, Product Development, and Infosec to integrate the platform with governed data sources and shared architectural contracts.\n Work with the Sr. Director and model evaluation tooling to help close the loop between evaluation results and model selection, prompt tuning, and routing decisions.\n \n What Skills and Knowledge Will You Bring?\n Ideal candidates will have:\n \n Hands-on experience building production services that sit in front of multiple consumers such as an API gateway, internal platform, or data platform, with real exposure to authentication/authorization, rate limiting, observability, or audit logging; direct AI and LLM platform experience is a strong plus but not required.\n Practical experience with agent orchestration frameworks (LangGraph or equivalent) and calling hosted model providers such as AWS Bedrock or Google Vertex, with a working understanding of how context windows, memory, and tool-calling actually behave in production, not just conceptually.\n 8 or more years of professional software engineering experience; with experience building or operating AI and ML infrastructure in a production environment.\n Strong Python skills, ideally with FastAPI, and comfort picking up frameworks like pydantic.ai for LLM-powered components; work","salary_min":184000,"salary_max":253000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["api-design","agents","cloud","security","llm","platform"],"apply_url":"https://www.sentinelone.com/jobs/?gh_jid=7805759003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-17T04:03:08Z","expires_at":"2026-08-19T14:30:58.778445Z","created_at":"2026-07-18T14:20:10.111105Z","updated_at":"2026-07-20T14:30:58.924156Z","company_name":"SentinelOne","company_slug":"sentinelone","company_logo_url":"https://www.google.com/s2/favicons?domain=sentinelone.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7a9c3c59-7703-4bde-a302-10e099cde6b9"},{"id":"76dfe6b0-19ed-4122-a27e-0b792fbb7bdd","company_id":"b459414f-fd43-42c4-a6e1-f07225286a75","title":"Evaluations Engineering - Member of Technical Staff","slug":"evaluations-engineering-member-of-technical-staff-0b12929a","description":"ABOUT THE COMPANY\n\nPilots don't train with real passengers. Actors don't rehearse with real audiences. Yet, the most consequential decisions in society are often pushed straight to production.\n\n\n\nSimile is changing that. We have built the first AI simulation of society, populated by generative agents based on real humans. Our research pioneered the field of AI-based simulation, proving it is possible to model human behavior with high accuracy. Today, we are developing a Foundation Model to predict human behavior in any situation, at any scale.\n\n\n\nWe are backed by $100M in funding led by Index Ventures, with participation from Hanabi, A*, Bain Capital Ventures, and AI visionaries including Andrej Karpathy, Fei-Fei Li, Adam D'Angelo, and Guillermo Rauch.\n\n\n\n\nABOUT THE ROLE\n\nAs a Member of Technical Staff in Evaluations Engineering, you will build the systems that enable Simile to evaluate whether our simulations of human behavior are accurate, trustworthy, and improving over time.\n\n\n\nYou will work across data and evaluation infrastructure, evaluation execution workflows, backend services, automation, and internal tooling. Your initial focus will include streamlining how evaluations are run across models; strengthening evaluation versioning, data models, and access controls; and automating customer validations, survey operations, and human data workflows.\n\n\n\nEvaluation at Simile presents unusual engineering challenges. Our models predict distributions of human behavior, and the ground truth used to evaluate them can be noisy and heterogeneous. You will partner closely with Evals, Modeling, Product Engineering, and Data Operations to turn complex methods and inputs into systems that are reproducible, scalable, and useful for model development and business decisions.\n\n\n\n\nIN THIS ROLE, YOU WILL:\n\n - Build evaluation execution infrastructure: Develop the services, pipelines, and orchestration needed to run evaluations efficiently across datasets, model versions, populations, and use cases.\n\n - Strengthen evaluation data systems: Design relational schemas, versioning, provenance, permissions, and quality controls that make evaluation results reproducible and trustworthy.\n\n - Automate validation and data collection: Partner with Evals and Data Operations to streamline customer validations, survey deployment, response ingestion, and the integration of new ground truth.\n\n - Build human data workflows: Create labeling and review tools that enable external experts and operators to contribute high-quality judgments to evaluation campaigns.\n\n - Develop evaluation tooling: Build interfaces that help teams manage evals, compare models, investigate results, and identify regressions.\n\n\n\n\nREQUIREMENTS\n\n\nMUST HAVES\n\n - Strong Engineering Fundamentals: Several years of experience building and maintaining production-quality software, with sound judgment in system design, testing, debugging, and maintainability.\n\n - Data and Systems Experience: Experience building backend services, data pipelines, automation workflows, and relational data models.\n\n - End-to-End Execution: Ability to work across data, backend, and interface layers and take ambiguous projects from technical design through deployment and adoption.\n\n - Evaluation Judgment: Strong intuition for what makes evaluation infrastructure reliable, including versioning, provenance, reproducibility, holdout integrity, noisy ground truth, and meaningful model comparisons.\n\n - ML and LLM Fluency: Familiarity with modern model-development and evaluation workflows sufficient to partner effectively with modeling and evaluation researchers.\n\n - Product and User Judgment: Ability to build clear, efficient tools for researchers, engineers, data operators, and other expert users.\n\n - Ownership and Communication: A track record of independently driving important technical work and collaborating effectively across engineering, research, and operations.\n\n\n\n\nNICE TO HAVES\n\nWe do not expect one person to have all of these. We are hiring a team with complementary strengths.\n\n - Model-Evaluation Infrastructure: Experience building LLM or ML evaluation systems, benchmark platforms, regression suites, experiment-tracking tools, or model-quality dashboards.\n\n - Research and Internal Tools: Experience developing technical surfaces for ML engineers, researchers, data scientists, or operations teams.\n\n - Human Data Systems: Experience with labeling platforms, expert-review workflows, LLM-as-judge systems, grader calibration, or other human-in-the-loop evaluation methods.\n\n - Data-Collection Automation: Experience automating surveys, experiments, customer-data ingestion, or other human data collection workflows.\n\n - Statistical Fluency: Comfort reasoning about sampling error, uncertainty, calibration, confidence intervals, and distributional metrics.\n\n - Sensitive Data and Access Controls: Experience designing permissions, auditability, and data-governance systems for human or customer data.\n\n - Agen","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","search","generative-ai","llm","agents","evaluation"],"apply_url":"https://jobs.ashbyhq.com/simile/beaa243c-233f-45f2-9e10-8d54e1eda9d1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-17T00:00:46.898Z","expires_at":"2026-08-19T14:22:22.33956Z","created_at":"2026-07-18T14:11:35.756548Z","updated_at":"2026-07-20T14:22:22.435742Z","company_name":"Simile","company_slug":"simile","company_logo_url":"https://www.google.com/s2/favicons?domain=simile.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/76dfe6b0-19ed-4122-a27e-0b792fbb7bdd"},{"id":"a6b2ea1b-7c66-4e50-bcdc-ffde68785dd9","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Scientist, Life Sciences (Computational)","slug":"research-scientist-life-sciences-computational-ddd6b9a2","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the team\n Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery.\n About the role\n We're seeking an exceptional Research Scientist to join the team. This role combines deep computational biology expertise with frontier AI capabilities, positioning Anthropic at the forefront of AI-driven scientific discovery.\n As one of the first computational members of this Life Sciences research group, you'll work on a high-impact team that operates at the intersection of computational and experimental biology. You'll bring broad computational biology experience to bear across the team's projects, driving discoveries from large-scale computational analysis of biological data through to results our experimental scientists can test, and moving flexibly between problems as the science demands. You'll have substantial access to Claude and you'll help establish how computational biology operates at Anthropic.\n This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for scientific understanding and biomedicine. If you're excited about using your computational expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you.\n Key responsibilities\n \n Build, run, and maintain the analysis pipelines that back the team's experimental programs: sequence analysis at petabyte scale, structural bioinformatics, phylogenetic and comparative genomics, design and analysis of high-throughput functional screens, biological sequence modeling, etc.\n Partner directly with experimental biologists to design experiments that produce high-quality data, and turn results around fast enough to immediately inform the next experiment\n Draw on the literature and curated biological knowledge bases alongside primary data to generate and prioritize hypotheses for experimental follow-up\n Stand up and maintain the team's computational infrastructure: data ingestion, workflow orchestration, internal databases, and the interfaces that make all of it accessible to both researchers and AI agents\n Use Claude and our internal agent frameworks heavily in your own work, and feed what you learn back to the model-improvement and product teams as evaluations, datasets, and concrete failure cases\n Pick up analyses across projects as priorities shift; we're looking for breadth and flexibility over a single deep specialty\n \n Minimum qualifications\n \n Have a PhD in computational biology, bioinformatics, genomics, biophysics, machine learning, computer science, or a related quantitative or biological field, or equivalent industry research experience\n Have a track record of computational biology research you have led end to end, from question to result, with evidence of impact (for example publications, preprints, released datasets or tools, or research that changed a program's direction)\n Have demonstrated breadth across multiple areas of computational biology\n Are proficient in one or more programming languages used in scientific computing and comfortable working on large datasets in Linux and cloud compute environments\n Can take an ambiguous biological question, scope the analysis, and produce a result an experimentalist can act on\n Communicate computational results clearly to both biologists and ML researchers\n \n Preferred qualifications\n \n Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments\n Are results-oriented, with a bias towards flexibility and impact\n Hands-on experience in experimental biology, or a track record of designing experiments side by side with experimentalists\n Experience building tools, pipelines, or agentic systems on top of LLMs, or training models on biological sequence data\n Deep expertise in one or two areas of computational biology (for example structural biology, metagenomics, single-cell genomics, or protein design) on top of the required breadth\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 $300,000 — $320,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education","salary_min":300000,"salary_max":320000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","agents","llm","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5357739008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T21:26:57Z","expires_at":"2026-08-19T14:00:31.014279Z","created_at":"2026-07-18T14:00:32.952102Z","updated_at":"2026-07-20T14:00:31.112768Z","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/a6b2ea1b-7c66-4e50-bcdc-ffde68785dd9"},{"id":"30c90b0d-0f42-411e-89c2-0a550ddbcfe9","company_id":"10df5a0b-95c8-4a89-a3a6-af92f87992f5","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-e3dd80a9","description":"About this opportunity: \n At Freenome, we are seeking a Senior Machine Learning Research Engineer to join the Machine Learning Science (MLS) team, within the Computational Science department. The ideal candidate has a strong knowledge in designing and building deep learning (DL) pipelines, and expertise in creating reliable, scalable artificial intelligence/machine learning (AI/ML) systems in a cloud environment.  \n The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimizing hardware utilization for efficient training, and performing model optimizations. As part of an interdisciplinary R\u0026D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission of reducing cancer mortality via accessible early detection.  \n The role reports to the Director of Machine Learning Science. This can be a hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote. \n What you’ll do: \n \n Implement and refine DL pipelines on distributed computing platforms enhancing the speed and efficiency of DL operations including model training, data handling, model management, and inference. \n Collaborate closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines you create are perfectly aligned with scientific goals and operational needs. \n Continuously monitor, evaluate, and optimize DL model training pipelines for performance and scalability. \n Stay up to date with the latest advancements in AI, ML, and related technologies, and quickly learn and adapt new tools and frameworks, if necessary. \n Develop and maintain robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results. \n Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation pipelines. \n Act as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement. \n \n Must haves: \n \n MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development.  \n 5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines. \n Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc. \n Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn. \n In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow.  \n Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment. \n Understanding of containerization technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions. \n Proven track record of developing and optimizing workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume. \n Experience managing large datasets, including data storage (eg: HDFS or Parquet on object storage), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark). \n Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows. \n Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team. \n Excellent ability to work effectively with cross-functional teams and communicate across disciplines.  \n \n Nice to haves: \n \n Experience working with large-scale genomics or biological datasets.  \n Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data. \n Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA). \n Experience with infrastructure-as-code and configuration management. \n Experience cultivating MLOps and ML infr","salary_min":161925,"salary_max":227325,"location":"Brisbane, California","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","pytorch","jax","distributed-systems","tensorflow","deep-learning","search","llm"],"apply_url":"https://job-boards.greenhouse.io/freenome/jobs/8535248002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T21:23:48Z","expires_at":"2026-08-19T14:26:09.917288Z","created_at":"2026-07-18T14:15:34.953947Z","updated_at":"2026-07-20T14:26:10.049571Z","company_name":"Freenome","company_slug":"freenome","company_logo_url":"https://www.google.com/s2/favicons?domain=freenome.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/30c90b0d-0f42-411e-89c2-0a550ddbcfe9"},{"id":"221462da-1fe9-437c-a353-d9eec3d41b17","company_id":"640dfef6-1316-46e7-a1df-2d0ef7b7ac59","title":"Senior Engineering Manager, AI Workflows","slug":"senior-engineering-manager-ai-workflows-aee47f88","description":"ABOUT SEMGREP\n\nSemgrep, the leader in code security for builders, empowers invention without friction. Teams catch, flag, and fix real issues before they ship, powered by security that learns as they build. Semgrep secures code as it’s written and provides guardrails that pave the road for developers to move fast and stay secure. Built for builders and trusted by security, Semgrep lives where developers work, delivering fixes without breaking flow, and giving security teams visibility, control, and confidence. Semgrep gets smarter as you build, with AI that learns your context to cut false positives and prioritize reachable vulnerabilities, validated by 95% of security reviewers across 6M+ findings. Semgrep makes zero false positives a reality with AppSec teams triaging 80% fewer false positives across Code and Supply Chain, dramatically shrinking the backlog.\n\n\nFounded in San Francisco and backed by Menlo Ventures, Felicis Ventures, Lightspeed Venture Partners, Redpoint Ventures, and Sequoia Capital, Semgrep is recognized by Gartner in Application Security Testing and is trusted by leading organizations, including Vanta, Lyft, and Dropbox. Learn more at semgrep.dev http://semgrep.dev.\n\n\n\n\nABOUT THE ROLE\n\nSemgrep is building a system called Workflows https://semgrep.dev/products/semgrep-workflows/ that combines traditional code security scanning tools with LLMs in innovative ways to detect, validate, and even remediate security issues better than traditional tools or LLMs on their own. This system is fundamental to how Semgrep is going to succeed in an AI-native world.\n\nThe workflows system consists of an SDK for composing operations (typically CLI tool calls and agent invocations) into workflows, an environment for periodically executing these workflows, and a set of pre-defined workflows that work out of the box. The workflows team owns all three of these areas (with assistance from our platform, infrastructure, and security research teams).\n\nWe are looking for an engineering manager to lead a team of 3-5 engineers and make their own contributions as well. You are ideal for this role if you have:\n\n - 3+ years of experience leading software engineering teams\n\n - Familiarity with agile development principles and iterative milestone development\n\n - A strong desire to help engineers and other leaders grow through coaching and mentorship\n\n - Ideally, you will have expertise in two of the following three areas:\n   \n   - Public SDK or API design and support\n   \n   - Data workflow orchestration (e.g. Metaflow or Argo workflows)\n   \n   - Applying AI to cybersecurity problems\n\nYou might spend a typical day:\n\n - Working with your team, product management, and engineering leadership to craft your team’s strategic direction and a strong quarter over quarter roadmap to execute on it\n\n - Defining goals within a team meeting to ensure your team is executing on their short term goals week over week while providing them the vision for the future of the product\n\n - Coaching a senior engineer, helping them gain the skills needed to lead and mentor other engineers through increasingly difficult projects\n\n - Making direct technical contributions to help deliver new features and gain a strong understanding of the work your team is doing\n\nThis is a hybrid role with the expectation you’ll join us 3+ days per week in our San Francisco office\n\n\n\n\nCOMPENSATION\n\nThe estimated starting annual salary range for this position is $197,000 to $288,000 USD. The actual base salary will be determined based on a number of factors, which may include job-related skills, relevant experience, qualifications, location, internal equity, and market data. In addition to base salary, total compensation may include equity, variable compensation, and benefits. We view equity as a meaningful part of our compensation philosophy and a way for employees to share in the long-term value they help create.\n\nCompensation ranges are reviewed regularly and may be adjusted as the role, individual performance, or market conditions evolve.\n\n\n\n\nWHAT WE OFFER (FTE ONLY)\n\nOur goal is to competitively and fairly compensate every Semgrep employee with a system that equally rewards those who are vocal and those who are less comfortable making demands during the final steps of the hiring process. To that end, we generate internal compensation bands that are used when discussing and negotiating salaries. We update these based on market data to make sure they’re above the average for comparable roles.\n\nWe invest in our employees’ well-being and long-term success through a competitive, market-aligned benefits program that meets or exceeds local market standards across all of the regions in which we hire. Benefits offerings vary by location to reflect local requirements and norms. For more detailed, location-specific information, please visit Semgrep Benefits https://www.notion.so/semgrep/Semgrep-Benefits-1593009241a88029bd7edf1fed1dfde2.\n\n\n\n\nWHO WE ARE\n\nWe bring toget","salary_min":197000,"salary_max":288000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","api-design","fine-tuning","security"],"apply_url":"https://jobs.ashbyhq.com/semgrep/e60bba6e-89d8-446a-a9fe-d84b1000327a/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T20:39:56.275Z","expires_at":"2026-08-19T14:23:12.164932Z","created_at":"2026-07-18T14:12:27.358155Z","updated_at":"2026-07-20T14:23:12.277724Z","company_name":"Semgrep","company_slug":"semgrep","company_logo_url":"https://www.google.com/s2/favicons?domain=semgrep.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/221462da-1fe9-437c-a353-d9eec3d41b17"},{"id":"9c93a171-98d2-45de-b402-ee329e58af5d","company_id":"10df5a0b-95c8-4a89-a3a6-af92f87992f5","title":"Staff Machine Learning Scientist","slug":"staff-machine-learning-scientist-101b9591","description":"About this opportunity: \n At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment. \n They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer. \n The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote. \n What you’ll do: \n \n Independently pursue cutting edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.). \n Build new models or fine-tune existing models to identify biological changes resulting from disease. \n Build models that achieve high accuracy and that generalize robustly to new data. \n Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms. \n Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration. \n Take a mindful, transparent, and humane approach to your work. \n \n Must haves: \n \n PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics.\n 6+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modeling techniques.\n Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modeling.\n Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation.\n Practical and theoretical understanding of DL models like large language models or other foundation models.\n Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning.\n Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data.\n Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.\n Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face.\n Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights \u0026 Biases.\n Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations.\n Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational biologists.\n A passion for innovation and demonstrated initiative in tackling new areas of research.\n \n Nice to haves: \n \n Deep domain-specific experience in computational biology, genomics, proteomics or a related field. \n Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models. \n Experience in NGS data analysis and bioinformatic pipelines. \n Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS. \n Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems. \n \n Benefits and additional information: \n The US target range of our base salary for new hires is $199,675 - $283,500.  You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.  Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ freenome.com/job-openings/  for additional company information.   \n Freenome is proud to be an equal-opport","salary_min":199675,"salary_max":283500,"location":"Brisbane, California","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","llm","tensorflow","deep-learning","generative-ai","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/freenome/jobs/8627491002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T20:05:13Z","expires_at":"2026-08-19T14:26:10.535252Z","created_at":"2026-07-18T14:15:35.357814Z","updated_at":"2026-07-20T14:26:10.643651Z","company_name":"Freenome","company_slug":"freenome","company_logo_url":"https://www.google.com/s2/favicons?domain=freenome.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9c93a171-98d2-45de-b402-ee329e58af5d"},{"id":"ac13ae57-2a6d-4da6-b903-7b0e4c813e09","company_id":"d8e15a46-b80d-4228-8e7b-34f00357f377","title":"Principal Product Manager Agents and Context - Elasticsearch","slug":"principal-product-manager-agents-and-context-elasticsearch-aeed2103","description":"Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.\n What is The Role \n Elastic, the Search AI Company, is looking for a Principal Product Manager to guide the vision, strategy, and execution for the Elastic Agent Builder. As organizations shift from traditional data retrieval to autonomous, agentic workflows, the Agent Builder serves as the critical context layer that enables users to make Agents faster, lower cost, and more accurate. In this high-impact role, you will be responsible for defining how enterprises build, manage, and scale context for AI agents. You will work at the cutting edge of the AI ecosystem, working with senior leadership and partnering with hyperscalers and evangelizing our solutions to a global community of AI developers.\n What You Will Be Doing \n \n Work directly with enterprise customers, sales teams, and solution architects to understand requirements, negotiate priorities, clarify product needs \n Build, socialize and align a roadmap for core context engineering capabilities built on top of Elastic powered retrieval and relevance for AI Agents \n Deeply understand the AI Agent market, major players, trends and how it may impact our strategy \n Work directly data science and engineering to build out the strategy for benchmarking and evaluations of agent capabilities \n Work with design to build user experiences that address gaps in how agents show and refine context as they work\n Be the product expert and evangelize capabilities for Agent Builder through content like blog posts and open source projects  \n Work with a broad ecosystem of AI partners including cloud service providers (Google, Amazon, Microsoft) and community developers \n \n What You Bring \n \n Extensive Experience: 10+ years of experience in product management or solution delivery for technical, cloud infrastructure, or platform products. A consistent record of leading sophisticated, data-intensive products from inception through launch and iterative growth.\n AI and ML Fluency: Deep technical understanding of the AI/ML landscape, including LLMs, RAG architectures, vector databases, and context engineering. You are comfortable working closely with engineers and data scientists to solve intricate technical challenges.   \n Bias to action: You are able to move fast and quickly learn from experiments and tests. You utilize AI tools to help accelerate your processes and bring clarity to your decisions. \n Leadership and Influence: Demonstrated ability to lead across a matrixed organization, align multiple stakeholders toward a common vision, and drive execution in a fast-paced, remote-first environment.   \n Communication Excellence: Outstanding spoken and written communication skills. You can distill complex engineering details into compelling narratives for both technical and non-technical audiences, including executive leadership.   \n Customer Obsession: A passion for industry trends and a commitment to solving real-world customer problems. You are an advocate for the developer persona and are dedicated to building products that empower users to innovate.\n Compensation for this role is in the form of base salary.  This role does not have a variable compensation component.  The typical starting salary range for new hires in this role is listed below. \n These ranges represent the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting.  We may ultimately pay more or less than the posted range, and the ranges may be modified in the future.  \n An employee's position within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs.\n Elastic believes that employees should have the opportunity to share in the value that we create together for our shareholders. Therefore, in addition to cash compensation, this role is currently eligible to participate in Elastic's stock program.  Our total rewards package also includes a company-matched Registered Retirement Savings Plan (RRSP) with dollar-for-dollar matching up to 6% of eligible earnings, along with a range of other benefits offered with a holistic emphasis on employee well-being.\n The typical starting salary range for this role is:\n $154,000 — $243,600 CAD","salary_min":154000,"salary_max":243600,"location":"Canada","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"principal","tags":["search","agents","rag","llm","embeddings","cloud"],"apply_url":"https://jobs.elastic.co/jobs?gh_jid=8070011\u0026gh_jid=8070011","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T18:21:48Z","expires_at":"2026-08-19T14:11:35.202831Z","created_at":"2026-07-18T14:09:28.766906Z","updated_at":"2026-07-20T14:11:35.379647Z","company_name":"Elastic","company_slug":"elastic","company_logo_url":"https://www.google.com/s2/favicons?domain=www.elastic.co\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ac13ae57-2a6d-4da6-b903-7b0e4c813e09"},{"id":"3f34c43f-1307-4759-b68f-8073291a1c99","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Staff Software Engineer, Foundation Model API","slug":"staff-software-engineer-foundation-model-api-b7d81082","description":"P-1930 \n At Databricks, we are passionate about enabling data and AI 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. Founded by engineers — and customer-obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.\n As part of the AI team, you'll build the platforms and products that power everything from data apps, AI agents, model training, model serving, and Vector Search. You'll be joining a high-agency, high-visibility team operating at the frontier of AI infrastructure — with deep ties to research, product, and real-world enterprise use cases. Databricks Mosaic AI is one of our fastest-growing businesses, helping thousands of our customers democratize AI within their organizations. We're building the products and infrastructure that power the next generation of AI.\n We're hiring across multiple teams in our AI Engineering org, including the FMAPI (Foundation Model APIs) team — the unified serving layer for large language models across real-time and batch inference, powering model inference at enterprise scale. We are looking to hire high-agency engineers who bridge the gap between technical execution and product strategy.\n The impact you will have: \n \n Build LLM infrastructure powering large-scale inference workloads for customers through partner models (OpenAI, Anthropic, Gemini) and self-hosted models (Qwen, GPT-OSS, Llama)\n Shape the direction of the FMAPI product — from roadmap to execution — by leveraging deep customer empathy and direct engagement with enterprise users and model providers\n Improve reliability, latency, and efficiency of distributed AI workloads\n Collaborate with platform, infra, and ML teams to deliver seamless end-to-end experiences\n Shape how developers and data scientists build and interact with AI on Databricks\n \n What we look for: \n \n 8+ years of experience in backend or infrastructure engineering\n Experience with distributed systems, scalable APIs, or cloud-native infrastructure\n Strong product and ownership mindset, with a focus on shipping user-facing value\n Experience with real-time serving, ML infrastructure, or GPU orchestration\n Familiarity with service-oriented architecture, deployment pipelines, and system observability\n Strong programming skills in Scala, Go, or Python\n \n Bonus points for: \n \n Exposure to platforms like SageMaker, Vertex AI, or Azure ML\n Built products that support AI workflows\n  \n Pay Range Transparency \n Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . \n  \n Local Pay Range\n $190,000 — $265,000 USD \n About Databricks \n Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on  Twitter ,  LinkedIn   and   Facebook . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . \n Our Commitment to Diversity and Inclusion \n At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-","salary_min":190000,"salary_max":265000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","cloud","generative-ai","mlops","agents","distributed-systems","data-pipeline"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8637143002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T18:21:12Z","expires_at":"2026-08-19T14:02:34.177799Z","created_at":"2026-07-18T14:02:34.2202Z","updated_at":"2026-07-20T14:02:34.270625Z","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/3f34c43f-1307-4759-b68f-8073291a1c99"},{"id":"4186a72e-fd16-4144-a3c5-962849116841","company_id":"46e2df02-755e-46ce-a584-3f5881f34183","title":"AI Engagement Lead","slug":"ai-engagement-lead-73578c8d","description":"About Turing \n Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage. \n Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. Learn more at  www.turing.com \n About the role:  \n Turing is looking for people with GenAI experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization leading a GenAI project end-to-end from POC to full-scale implementation and you will be leading a team of other Turing engineers across different skill sets. In the past, the Turing GenAI delivery organization has implemented industry-leading multi-agent LLM systems, RAG systems, and Open Source LLM deployments for major enterprises.\n Role responsibilities \n \n Client Relationship and Communication: \n \n Lead discussions with clients and prospects to build AI product pipeline and expand to more processes.\n Prioritize client engagement, strengthen relationships, and manage expectations proactively.\n Act as a point of contact for client communication, feedback and escalations.\n Evaluate and select use-cases for new product development and unlock new revenue opportunities for the client\n Participate in client’s internal stakeholder meetings (when applicable) to capture, clarify, and consolidate requirements across functions, ensuring all inputs are properly documented and mapped into actionable product needs.\n \n Tech Leadership:\n \n Provide technical direction to the engineering team on appropriate solutioning \u0026 system design for a given problem statement\n Align the engineering team toward a technical roadmap and ensure timely execution of the roadmap to achieve customer satisfaction. \n Study new LLM research topics and engage in conversation on same with client stakeholders to showcase technical prowess and build value for Turing.\n \n Team Leadership and Coordination:\n \n Drive cross-functional alignment across engineering, product, and client success teams.\n Remove blockers for both the client \u0026 internal teams by ensuring smooth communication and effective prioritization flows.\n Conduct regular 1:1 meetings focused on support, expectations, delivery alignment, and general well-being.\n \n Project Planning and Oversight:\n \n Manage the big-picture program timeline (releases, phases, go-live plans) using engineering velocity and capacity inputs provided by the Engagement Manager.\n Co-own delivery planning by managing the program-level timeline and facilitating Sprint Planning sessions ensuring release milestones and sprint goals are aligned with business priorities, clearly defined requirements, and the engineering capacity and feasibility inputs provided by the Engagement Manager.\n \n \n Own overall delivery to ensure quality, scope, and timelines are consistently met.\n Ensure robust business-facing documentation , including requirements documents, BRDs/PRDs, implementation plans, product roadmaps, and client-facing marketing or enablement materials.\n Ensure delivery decisions reflect  an understanding of operational costs, ROI, and long-term business impact .\n Identify delivery risks , create proactive mitigation plans, and track program health across all milestones with keen insight.\n Acknowledge new client requests promptly and partner with the Engagement Manager to assess feasibility, capacity, and timeline impact before providing any commitment to the client.\n \n \n Required skills \n \n Excellent communication skills \u0026 stakeholder management to effectively collaborate with client SMEs\n Entrepreneurial \u0026 founders mindset to own end-to-end GenAI project deliveries and guide customers through various phases\n 10+ years of professional experience with at least 4+ years in driving Machine Learning \u0026 AI projects\n Good understanding of the latest GenAI agentic designs and frameworks like Langchain, Langgraph, etc.\n Understanding of Cloud services including Azure, GCP, or AWS\n Understanding of Agile methodologies and project management tools like JIRA, Confluence.\n \n Compensation Range : $240k - $260K Location: Remote or NYC  \n Values \n \n \n We are client first : We put our clients at the center of everything we do, because their success is the ultimate measure of our value.\n \n We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection","salary_min":240000,"salary_max":260000,"location":"United States","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["rag","agents","generative-ai","llm"],"apply_url":"https://job-boards.greenhouse.io/turing/jobs/6117693004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T16:17:16Z","expires_at":"2026-08-19T14:05:11.890219Z","created_at":"2026-07-18T14:05:12.620898Z","updated_at":"2026-07-20T14:05:12.066458Z","company_name":"Turing","company_slug":"turing","company_logo_url":"https://www.google.com/s2/favicons?domain=turing.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4186a72e-fd16-4144-a3c5-962849116841"},{"id":"7aa6bdf1-9f05-454e-a2e7-9a8ad35a91c5","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-4010bcc3","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is a fully remote position based in Toronto, Canada. \n At Recursion, we believe that every employee should be compensated fairly. Based on the skills, experience, and qualifications needed for this role, the estimated annual base salary range is:  $210,070–$282,851 (CAD) . In addition to base salary, this role is eligible for an annual bonus, equity compensation, and a comprehensive benefits package.\n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. 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Generating convincing phishing pages, spinning up realistic-looking fraudulent accounts, and producing abusive content at scale no longer requires specialized skill — a motivated attacker with access to widely available models can do it.\n\nAt the same time, AI is transforming what's possible on the defensive side. A small, sharp T\u0026S team equipped with frontier models can now build detection, triage, and response systems that would have required a team five times the size just a couple of years ago. AI-assisted content scanning, automated investigation workflows, and intelligent alert prioritization mean that a lean team can protect millions of users — if they build the right infrastructure.\n\n\n\n\nABOUT THE ROLE\n\nYou'll architect and scale the trust infrastructure that keeps 70M+ users safe while preserving the creative freedom that makes Gamma magical. This means designing distributed systems for real-time content scanning, anomaly detection, and rate limiting; helping define and evolve the core data model and storage systems behind abuse detection; and shipping the high-volume event pipelines and internal tools that let our support teams act quickly on threats.\n\nYou'll join a small, high-impact team building the foundation for trust at scale. You'll work across databases, public APIs, and event infrastructure, balancing long-term technical investments with rapid shipping velocity. You'll collaborate across engineering, product, and design to define how Gamma approaches safety for the long term.\n\nOur team has a strong in-office culture and works in person 4–5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most.\n\n\n\n\nWHAT YOU'LL DO\n\n - Own detection and prevention systems for fraud, abuse, spam, and malicious content across millions of daily users\n\n - Design and build scalable trust infrastructure including rate limiting, content scanning, anomaly detection, and account security\n\n - Architect distributed systems, APIs, and high-volume event pipelines that power real-time abuse detection at Gamma's scale\n\n - Help define and evolve the core data model and storage systems that underpin trust and safety across the product\n\n - Build tools that empower internal support teams to investigate and act on suspicious or malicious activity\n\n - Leverage AI/LLM-based detection to stay ahead of AI-generated abuse — phishing, fraud, and malicious content that's increasingly indistinguishable from legitimate activity\n\n - Build automated triage and investigation workflows that let a small team operate at the scale of a much larger one\n\n - Investigate and resolve complex security incidents with limited context\n\n - Partner with engineering and product to balance security with user experience\n\n - Shape Gamma's long-term trust and safety strategy and technical roadmap\n\n\n\n\nWHAT YOU'LL BRING\n\n - 5+ years of backend engineering experience building scalable, high-traffic production systems\n\n - Strong systems thinking and experience building highly available web APIs\n\n - Strong proficiency in backend technologies (Node.js, Python, or similar) and databases (PostgreSQL, Redis)\n\n - Experience with event streaming systems (Redis, Kafka, or similar) and high-volume event pipelines\n\n - Hands-on experience implementing trust features like rate limiting, content detection, and fraud prevention\n\n - Track record shipping high-quality, complex applications under tight timelines\n\n - Product-minded approach with understanding of how technical decisions impact user experience and business metrics\n\n - Passion for security, user protection, and solving problems at scale\n\nNice to have\n\n - Experience with AI/LLMs for content moderation, or familiarity with TypeScript, Prisma, Apollo GraphQL, or AWS\n\n - Experience building AI-assisted moderation pipelines or using frontier models for automated abuse detection and response\n\n - Familiarity with the evolving AI threat landscape — how generative models are being used for phishing, social engineering, and content abuse at scale\n\n - Experience with real-time collaboration systems\n\n\n\n\nCOMPENSATION RANGE:\n\nThe base salary for this full-time position, which spans multiple internal levels depending on qualifications, ranges between $180K–$310K plus benefits \u0026 equity.\n\n\n\nFinal offer amounts are determined by multiple factors, including but not limited to experience and expertise in the requirements listed above.\n\n\n\nIf you're interested in this role but you don't meet every requirement, we encourage you to apply anyway! 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