{"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","gpu","code-generation","distributed-systems","search","jax","fine-tuning","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":"2026-06-29T14:00:21.52187Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-05-30T14:00:21.633054Z","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":"ABOUT xAI \n xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \n ABOUT THE ROLE:\n You will join the 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 xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n xAI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":150000,"salary_max":450000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["speech","reinforcement-learning","pre-training","pytorch","fine-tuning","distributed-systems"],"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":"2026-06-29T14:02:58.935925Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-05-30T14:02:59.041832Z","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":"a3d16455-f42f-4915-8723-2d023a5b665b","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Software Engineer II, AI Labs \u0026 Foundations","slug":"senior-software-engineer-ii-ai-labs-foundations-e74eb4cd","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview\n Join Instacart's mission to transform grocery shopping through frontier AI. As a Senior Software Engineer on AI Labs \u0026 Foundations, you will design, build, and operate the high-scale production systems that power our most ambitious AI experiences—from Cart Assistant, our conversational shopping agent, to voice AI interactions and beyond. This is a high-impact opportunity to work at the intersection of robust software engineering and cutting-edge production AI/ML, directly shaping products used by millions of customers every day.\n We are hiring a Senior Software Engineer who will participate in the design and delivery of production AI systems, identify high-leverage technical opportunities, and contribute hands-on to AI-native products across Instacart's platform. We value bottom-up ideas, high engineering quality, and close partnership with Product, Data Science, ML, and Infrastructure teams. If you enjoy inventing, navigating ambiguity, prototyping fast, and turning wild ideas into real, scalable products, this is the team for you.\n AI Labs \u0026 Foundations sits at the intersection of frontier AI research and production engineering. Our portfolio spans the full stack of AI innovation at Instacart, including building and launching Cart Assistant, pioneering voice AI interactions, and constructing the foundational systems that power these cutting-edge experiences. We are a fast-moving, collaborative team that thrives on 0-to-1 thinking, shares learnings openly, and ships with urgency by prototyping fast and testing rigorously.\n About the Job\n \n Design, build, and operate production AI-powered systems and agentic experiences (including Cart Assistant and voice AI) that directly impact how millions of customers shop.\n Build foundational systems for cutting-edge AI experiences, ranging from embedding infrastructure and voice AI pipelines, to client facing components and integrations, by prototyping bold ideas and productizing what works.\n Integrate foundation models via APIs and open-source frameworks; apply techniques like retrieval-augmented generation and vector search where appropriate.\n Own projects end-to-end: requirements, technical design, implementation, testing, deployment, observability, and iterative improvement focused on reliability, latency, and cost efficiency.\n Collaborate with cross-functional partners in product, design, data science, and infrastructure to ship AI features end-to-end.\n Drive engineering excellence, including thoughtful system design, rigorous code review, and technical leadership that includes defining and promoting best practices for AI/ML production engineering across the team.\n \n About You\n Minimum Qualifications: \n \n Proven senior software engineer who has built, shipped, and operated production systems at scale. You make architectural calls, own what you build, and deliver through ambiguity.\n Hands-on experience with AI or ML in production. You've shipped LLM-powered features or integrated foundation model APIs into a live product, demonstrating the necessary expertise at the intersection of robust software engineering and deep production ML.\n Experience owning services end-to-end, including CI/CD, automated testing, observability (logging, metrics, tracing), and on-call participation.\n Strong communicator who partners well across disciplines - you want to get to the right answer, not just defend the first one.\n Excitement and ability to leverage cutting-edge development tools, including AI assistance (e.g., Copilot, Cursor, Claude), to maximize velocity.\n \n Preferred Qualifications: \n \n 5 to 8+ years of industry experience.\n A track record of 0-to-1 work taking unconventional ideas from prototype through rapid iteration to production.\n Experience building conversational agents, multi-turn dialogue systems, or agentic LLM applications.\n Exp","salary_min":192000,"salary_max":202000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["cloud","fine-tuning","code-generation","generative-ai","llm","distributed-systems","agents","speech"],"apply_url":"https://instacart.careers/job/?gh_jid=7951041","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T22:43:14Z","expires_at":"2026-06-29T14:08:42.057285Z","created_at":"2026-05-30T14:08:42.180879Z","updated_at":"2026-05-30T14:08:42.180879Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a3d16455-f42f-4915-8723-2d023a5b665b"},{"id":"09d0acb5-52de-4a76-88c6-0eb844785025","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Research Scientist - RL Training","slug":"research-scientist-rl-training-ffdbae39","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n ABOUT THE ROLE  \n We're looking for a Research Scientist to work on reinforcement learning for training and aligning large language models. This is a foundational research role focused on one of the most consequential open data problems in AI: how to generate the data, reward signals, and training procedures that steer LLM behavior in reliable and generalizable directions — and a core capability that directly differentiates Snorkel's data-as-a-service offering. \n You'll work closely with Snorkel's research, engineering, and delivery teams to advance our RL data capabilities — translating research ideas into the preference datasets, reward models, and RL-ready corpora we produce for frontier AI labs, and contributing to a research agenda that is central to Snorkel's long-term differentiation as a provider of bespoke training data. \n MAIN RESPONSIBILITIES  \n \n Research and implement reinforcement learning techniques — including GRPO, RLHF, RLAIF, DPO, and reward modeling — and translate them into data products (preference datasets, reward signals, verifiable rewards) that customers can use to train and fine-tune large language models. \n Design and build data pipelines that generate high-quality training signal for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks . \n Prototype and iterate on end-to-end RL training recipes that inform what data Snorkel ships as part of its data-as-a-service deliveries. \n Work closely with research scientists, ML engineers, and delivery teams to translate RL research into customer-ready data products.\n Stay current with the latest developments in large-scale muli-node LLM training, alignment research, and scalable RL methods (on complex environments such as Terminal-Bench), bringing relevant advances into Snorkel's data-as-a-service approach.\n Contribute to Snorkel's research publications and internal knowledge base in RL and model training.\n \n PREFERRED QUALIFICATIONS  \n \n Deep expertise in reinforcement learning from human or AI feedback, reward modeling and credit attribution ideally with a clear perspective on what data makes these techniques work. \n Experience training or fine-tuning 30B+ large language models at scale, including familiarity with distributed training infrastructure. \n Strong proficiency in Python and ML frameworks, especially PyTorch and HuggingFace and hands-on experience with RL frameworks such as Verl and SkyRL. \n Solid software engineering fundamentals — you can build research prototypes that others can run, extend, and integrate into data production workflows. \n Familiarity with ML infrastructure and cloud platforms and tools (AWS, GCP, Kubernetes, Slurm, etc.); experience with large-scale RL training pipelines a strong plus. \n Comfort operating in a high-iteration environment with open-ended research questions and shifting, customer-driven technical constraints. \n Ph.D. in machine learning, reinforcement learning, or a related field strongly preferred; exceptional industry experience considered. \n Salary Range \n $200,000 — $325,000 USD \n Be Your Best at Snorkel \n Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.\n Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and haras","salary_min":200000,"salary_max":325000,"location":"Redwood City, CA","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["alignment","distributed-systems","pytorch","fine-tuning","generative-ai","data-pipeline","llm","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6009496004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T21:22:40Z","expires_at":"2026-06-29T14:03:05.747327Z","created_at":"2026-05-30T14:03:05.857458Z","updated_at":"2026-05-30T14:03:05.857458Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/09d0acb5-52de-4a76-88c6-0eb844785025"},{"id":"14a818b5-1068-4d53-8e01-2106c013d919","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Software Engineer, Operational/ Process Efficiency ","slug":"software-engineer-operational-process-efficiency-0675432b","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n This role is at the intersection of robotics and machine learning, driving the next generation of operational efficiency for Waymo's rapidly expanding autonomous fleet. You will lead efforts to generalize complex depot operations—such as exterior cleaning, sensor calibration, and maintenance checks—using advanced robotics. Key work involves leveraging foundation models, reinforcement learning, simulation, and integrating ML models in production at scale. You will interface closely with operations teams to translate real-world needs into robust, working solutions.\n This role follows a hybrid work schedule and reports to a Director, Hardware and Sensors. \n You will: \n \n Drive the automation of the hardware lifecycle for critical sensors (lidar, radar, cameras) and compute modules.\n Develop and deploy agentic systems and foundation models to streamline workflows between internal teams and contract manufacturers.\n Identify opportunities to apply AI to manufacturing, installation, and troubleshooting processes to increase operational velocity.\n Interface with a diverse set of stakeholders, including hardware design engineers, failure analysis engineers, and diagnostic teams, to translate physical requirements into technical specifications.\n Bridge the gap between experimental ML models and high-scale production environments.\n \n You have: \n \n A Masters or PhD in Machine Learning, Computer Science, or a related technical field.\n A proven track record of delivering working engineering solutions, balancing scientific rigor with production needs.\n Experience in training, evaluating, and deploying machine learning models at scale.\n Strong communication skills and the ability to collaborate across multidisciplinary teams (from field technicians to hardware designers).\n \n We prefer: \n \n Hands-on experience or deep familiarity with agentic tools and frameworks.\n Experience working with large-scale foundation models (LLMs, VLMs) and fine-tuning them for specialized domains.\n Background in automating industrial or hardware-centric workflows.\n Familiarity with hardware diagnostics, failure analysis, or manufacturing processes.\n \n  \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"hybrid","job_type":"full-time","experience_level":"mid","tags":["agents","generative-ai","robotics","autonomous-vehicles","llm","reinforcement-learning","fine-tuning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7926526","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T20:26:39Z","expires_at":"2026-06-29T14:04:30.317025Z","created_at":"2026-05-30T14:04:30.42607Z","updated_at":"2026-05-30T14:04:30.42607Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/14a818b5-1068-4d53-8e01-2106c013d919"},{"id":"c4efb161-7f94-4ca1-8b5e-49dba720ed4a","company_id":"01048ffd-9864-41e0-a719-14b849fbcbcd","title":"Mission Integration Engineer, AI (Starshield)","slug":"mission-integration-engineer-ai-starshield-f7ba9f64","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n MISSION INTEGRATION ENGINEER, AI (STARSHIELD) \n Special Programs leverages technology and launch capability to support national security efforts. Starshield, within Special Programs, is designed for government use, with an initial focus on earth observation, communications, and hosted payloads.\n The data applications team is building highly reliable mission-critical AI solutions supporting Starshield use-cases. You will drive the development and enhancement of core applications while collaborating cross-functionally to deliver end-to-end products. Aerospace experience is not required to be successful here—we want our engineers to bring fresh ideas from all areas. We look for engineers who love solving problems and seek to make an impact on an inspiring mission. As we expand this team, we’re looking for versatile, motivated, and collaborative engineers.\n Our team is responsible for identifying pain points, scoping product specifications, and designing and building LLM-powered software for government or enterprise use cases. This role will enhance model performance through system prompt tuning or fine-tuning, with an eye toward secure and scalable deployment.\n RESPONSIBILITIES: \n \n Develop full-stack solutions to manage scalable AI content interaction systems with user applications\n Develop prototypes to prove key design concepts and quantify technical constraints\n Apply creativity to build an immersive earth observation experience for users whether they are in offices or in the field\n See your software through from start-to-finish: from figuring out the core needs to prototyping, developing, and testing; to production rollout and beyond\n \n BASIC QUALIFICATIONS: \n \n Bachelor’s degree in computer science, data science, physics, mathematics, or engineering discipline\n Experience with Javascript libraries such as React, Angular, and/or Redux\n Professional experience developing Python applications\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Professional experience developing web services and distributed applications across cloud and on-premise networks\n Professional experience developing within Linux server environments, SSH, scripting, and configuration\n Experience with Kubernetes or similar container orchestration frameworks\n Experience with Model Context Protocol or large model content interaction systems\n Experience with developing RAG pipelines, tool callers, and AI orchestration systems\n Experience with Vector databases or implementing highly performant databases\n Experience with image data processing and machine learning\n Experience with graphics-intensive web application development\n \n ADDITIONAL REQUIREMENTS: \n \n Must be willing to work extended hours and weekends as needed\n Active Top Secret, Top Secret SCI, or DOE Level Q clearance\n An active clearance may provide the opportunity for you to work on sensitive SpaceX missions; if so, you will be subject to pre-employment drug and random drug and alcohol testing\n \n  \n COMPENSATION AND BENEFITS: \n Pay Range:   Mission Integration Engineer/Level I: $100,000.00 - $115,000.00/per year. Mission Integration Engineer/Level II: $110,000.00 - $135,000.00/per year.     \n Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience. Those with an active clearance will receive a 10% differential, up to an additional $20,000 annually, once officially briefed into a classified program.\n Base salary is just one part of your total rewards package at SpaceX. You may also be eligible for long-term incentives, in the form of company stock, stock options, or long-term cash awards, as well as potential discretionary bonuses and the ability to purchase additional stock at a discount through an Employee Stock Purchase Plan. You will also receive access to comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, paid parental leave, and various other discounts and perks. You may also accrue 3 weeks of paid vacation \u0026 will be eligible for 10 or more paid holidays per year. Employees accrue paid sick leave pursuant to Company policy which satisfies or exceeds the accrual, carryover, and use requirements of the law.\n ITAR REQUIREMENTS: \n \n To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the IT","salary_min":110000,"salary_max":135000,"location":"Hawthorne, CA","workplace":"onsite","job_type":"full-time","experience_level":"mid","tags":["embeddings","fine-tuning","rag","llm"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8569184002?gh_jid=8569184002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T21:57:12Z","expires_at":"2026-06-29T14:16:59.105031Z","created_at":"2026-05-29T15:07:10.614713Z","updated_at":"2026-05-30T14:16:59.217294Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c4efb161-7f94-4ca1-8b5e-49dba720ed4a"},{"id":"b2263952-2d61-4a59-acd2-4d8506c9b16e","company_id":"cf6855a4-6591-475f-be10-f3f36cf31758","title":"Senior Software Engineer, Search Relevance","slug":"senior-software-engineer-search-relevance-8f221ba2","description":"WHAT IS BOX?  \n Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.\n By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.\n WHY BOX NEEDS YOU \n The Search Relevance team at Box powers discovery across billions of files, enabling customers to find the right content quickly, securely, and intelligently. As we expand into a new era of AI-powered content understanding, we’re investing in the foundation that makes great search possible: reliable systems, strong signals, and models that learn from real-world usage.\n This is a rare opportunity to work at the intersection of information retrieval science, applied machine learning, and large-scale distributed systems. You’ll be building the infrastructure that powers intelligent content discovery for Fortune 500 companies—where milliseconds matter, relevance is measurable, and your experiments directly impact how millions of users work.\n We’re looking for a Senior Software Engineer to elevate search quality end-to-end—signals, ranking, retrieval, and evaluation—while building scalable, low-latency services that serve queries in real time. You’ll partner with Product, Data, and Infra teams to productionize cutting-edge models and experimentation frameworks, and help define the future of Box’s content intelligence, including hybrid and semantic search and our next-generation content agent.\n WHAT YOU'LL DO  \n \n Build and improve ranking, retrieval, and recommendation systems; identify the right signals and metrics to drive quality improvements that users can feel.\n Apply cutting-edge techniques (embeddings, LLM-enabled retrieval, hybrid search) to productionize experimentation and evaluation pipelines that scale to trillions of documents.\n Define and execute offline/online evaluation, A/B testing, and relevance tuning (NDCG, MRR, precision@k) to continuously improve search outcomes.\n Develop infrastructure for low-latency, high-availability query serving and near real-time indexing across distributed systems.\n Tackle distributed systems challenges including data sharding, intelligent routing, replication, and performance optimization.\n From ETL pipelines and feature engineering to model serving and result ranking—understand how data flows through the system and optimize at every stage.\n Lead design and implementation of new platform components from the ground up; establish patterns, raise the bar on code quality, and champion best practices.\n Share your expertise, contribute to technical direction, conduct thoughtful code reviews, and help shape our engineering culture.\n Participate in our on-call rotation, available at all times while on-call to help respond to and triage any issues that arise.\n \n WHO YOU ARE  \n \n 5+ years of industry experience building and operating backend or distributed systems at scale.\n Strong proficiency in an object-oriented language (e.g., Java, Scala, C++, or Python); Python experience strongly preferred.\n Hands-on experience building ranking, recommendation, NLP, or applied AI platforms in production. You understand the ML lifecycle from training to serving.\n Comfortable with data pipelines, message queues, and/or streaming systems (e.g., Kafka, Pub/Sub) and near real-time data processing.\n Experienced deploying and operating microservices in cloud environments; solid grasp of reliability, observability, and performance best practices.\n BS in Computer Science or related field, or equivalent practical experience.\n AI-first mindset—pragmatic about using the right models, signals, and evaluation methods to improve outcomes quickly and measurably.\n \n PREFERRED \n \n Experience with Elasticsearch, Solr, Lucene, or building custom search systems; deep understanding of inverted indexes, scoring functions, and query optimization.\n Knowledge of ML relevance tuning, learning-to-rank, retrieval evaluation metrics, offline/online testing, and A","salary_min":198500,"salary_max":248000,"location":"Redwood City, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["search","tensorflow","distributed-systems","pytorch","llm","nlp","fine-tuning","mlops"],"apply_url":"https://job-boards.greenhouse.io/boxinc/jobs/7926452","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T19:12:52Z","expires_at":"2026-06-29T14:19:20.83221Z","created_at":"2026-05-29T15:11:42.002134Z","updated_at":"2026-05-30T14:19:20.940887Z","company_name":"Box","company_slug":"box","company_logo_url":"https://www.google.com/s2/favicons?domain=box.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b2263952-2d61-4a59-acd2-4d8506c9b16e"},{"id":"64170ac3-3bc0-4e64-aa55-14d395814525","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Machine Learning Engineer II, Ads Response Prediction","slug":"senior-machine-learning-engineer-ii-ads-response-prediction-c8a2de33","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core ML models that power Instacart’s ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will also have the opportunity to shape our next-generation foundation model approach for ads ranking and contribute to cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models.\n The Ads Response Prediction team owns all systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, sequential modeling and generative retrieval systems for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue.\n The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure.\n About the Job \n \n Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces.\n Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases.\n Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning.\n Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements.\n Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction.\n Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.\n Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.\n \n About You \n Minimum Qualifications \n \n PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.\n 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale.\n Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep \u0026 Wide, DeepFM, DCN, and multi-task learning formulations.\n Strong foundation in causal inference, counterfactual reasoning, and","salary_min":201000,"salary_max":212000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["llm","pytorch","deep-learning","generative-ai","data-pipeline","mlops","fine-tuning","distributed-systems"],"apply_url":"https://instacart.careers/job/?gh_jid=7963838","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T19:10:27Z","expires_at":"2026-06-29T14:08:41.426586Z","created_at":"2026-05-29T14:32:35.186075Z","updated_at":"2026-05-30T14:08:41.541147Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/64170ac3-3bc0-4e64-aa55-14d395814525"},{"id":"152a9a3c-a62a-4f20-a505-60c62517b468","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Machine Learning Scientist, Multimodal AI ","slug":"machine-learning-scientist-multimodal-ai-e50612bb","description":"POSITION SUMMARY: \n Natera is hiring a Machine Learning Scientist to join our AI and computational biology team. This role develops and deploys deep learning models across digital pathology, genomics, transcriptomics, and cell-free DNA (cfDNA) modalities. You will build multimodal AI systems that integrate imaging, molecular, and clinical data, leveraging proprietary genomic and clinical datasets. You will collaborate with scientists, pathologists, bioinformaticians, and software engineers to scale machine learning approaches that advance personalized oncology diagnostics and tumor-informed minimal residual disease (MRD) testing.\n PRIMARY RESPONSIBILITIES: \n \n Design, implement, and evaluate deep learning models across biomedical data modalities, including histopathology imaging, genomic sequencing, transcriptomics, and cfDNA features\n Develop multimodal AI architectures that integrate H\u0026E whole-slide imaging data with molecular and clinical data sources\n Build scalable, production-quality machine learning workflows and pipelines using cloud infrastructure (AWS)\n Apply modern machine learning techniques including convolutional neural networks (CNNs), vision transformers (ViTs), sequence transformers, representation learning, and foundation model fine-tuning\n Collaborate across technical and clinical teams to translate machine learning prototypes into validated tools\n Analyze model outputs to generate reproducible biological and clinical insights\n Document pipelines thoroughly and communicate data-driven findings clearly to cross-functional stakeholders\n \n QUALIFICATIONS: \n \n PhD in Computer Science, Computational Biology, Biomedical Engineering, Bioinformatics, Statistics, or a related quantitative discipline with a focus on machine learning or AI\n Core experience developing machine learning models for biomedical applications, specifically in medical imaging, computational pathology, genomics, transcriptomics, multi-omics, or molecular diagnostics\n Hands-on expertise with PyTorch and strong production-level programming skills in Python\n Practical application of deep learning architectures such as CNNs, transformers, attention mechanisms, and representation learning\n Experience managing datasets and training workflows within distributed or cloud computing environments (AWS)\n Proven ability to take ownership of research projects and translate prototypes into robust, deployment-ready workflows\n Experience adapting pre-trained foundation models for downstream biomedical applications\n \n PREFERRED QUALIFICATIONS: \n \n Experience integrating imaging, molecular, and clinical data within unified multimodal machine learning frameworks\n Technical familiarity with DNA sequencing, RNA sequencing, methylation, and ctDNA assays\n Hands-on experience with digital pathology software and whole-slide imaging analysis\n Exposure to survival modeling, longitudinal prediction, or time-to-event modeling\n Experience applying self-supervised learning, weakly supervised learning, or multiple instance learning (MIL) to clinical data\n Domain knowledge in oncology, biomarker discovery, or clinical precision medicine\n Track record of peer-reviewed publications in machine learning or computational biology conferences and journals (e.g., NeurIPS, ICML, CVPR, MICCAI, Nature Biomedical Engineering)\n \n #LI-DNI\n  \n The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years \u0026 depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.\n Remote USA\n $124,800 — $156,000 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more informatio","salary_min":124800,"salary_max":156000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"mid","tags":["deep-learning","pytorch","healthcare","fine-tuning","generative-ai","cloud","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6004385004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T18:47:01Z","expires_at":"2026-06-29T14:10:20.275908Z","created_at":"2026-05-29T14:38:23.474911Z","updated_at":"2026-05-30T14:10:20.386097Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/152a9a3c-a62a-4f20-a505-60c62517b468"},{"id":"a1e6623a-57bb-42a8-964e-e2b9698f0e35","company_id":"e455f75a-a424-4955-9844-afebe8ea6eb4","title":"Senior Forward Deployed Engineer","slug":"senior-forward-deployed-engineer-ff299cc4","description":"Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.\n About Okta for AI Agents \n Okta secures access for 20,000 organizations and billions of users. Okta for AI Agents extends that work to the agentic shift. Deploying an AI agent is not like deploying traditional software. You are putting professional work output into production, and it needs deep integration, continuous tuning, and change management. Every agent needs an identity, a scope, an audit trail, and a way to be shut down when it goes wrong. Most enterprises have not built this yet. We are.\n We hire builders who see the cracks in enterprise agent identity that everyone else has learned to live with.\n The Role \n You embed inside four to five of Okta’s most strategic enterprise customers as their dedicated technical partner for agent identity. You sit alongside their identity, platform, and security engineering teams, write production code in their environment, and own the technical outcome from prototype through production.\n You are a builder-consultant. You go past architecture diagrams to code, debug, and ship bespoke agent identity solutions inside the customer’s environment. You ship secure agents faster for the customer, and you feed real field insight back to Okta product engineering.\n Responsibilities \n \n Become the customer’s trusted technical voice on agent security. Sit in their standups, design reviews, and incident response. Earn a seat on their architecture review board and security council for agent risk decisions.\n Architect and deploy with the customer’s team. Build Okta’s agent security stack into their infrastructure: Cross-App Access (XAA), Fine-Grained Authorization (FGA), MCP Gateway, and agent client registration. Own the identity, delegation, audit, and kill-switch architecture end to end, and coach their engineers on the patterns.\n Engage senior leadership. Brief the CISO, CIO, identity leaders, Chief AI Officer, and principal architects. Translate token-exchange flows into board-level agent risk, and AI governance mandates into architecture.\n Deliver white-glove deployment. Agents in production with full identity coverage, security review passed, governance requirements met, and posture visibility online. The customer points to you as the reason their agent program is real.\n Keep deployments defensible. Align architecture decisions to OWASP Top 10 for Agentic Applications, NIST AI RMF, and MITRE ATLAS, and to HIPAA, FedRAMP, or SOC 2 where the customer is regulated.\n Wire Okta into the customer’s stack. Connect O4AA to their IdP for human-to-agent links, IGA for agent lifecycle, ISPM for posture, SIEM and EDR for behavior coverage, and policy engines for runtime decisions.\n Build evals and observability. Authorization decision latency, scope sprawl across agents, anomalous delegation chains, audit completeness, kill-switch verification, and rogue agent detection.\n Turn field patterns into product. Extract the recurring gaps from their architects and governance leads, and convert them into reusable modules and roadmap fixes that ship for every other customer.\n Be on site. Regular presence at customer locations. Trust and governance alignment happen in the room.\n \n Requirements \n \n Engineering pedigree. 7+ years shipping production software, still hands-on in the IDE, with on-call experience and operational maturity in systems that authenticate and authorize at high throughput.\n Identity protocols. OAuth 2.0, OIDC, SAML, SCIM, RFC 8693 token exchange, act claims, CIMD and DCR, DPoP.\n Agent security frameworks. Working knowledge of OWASP Top 10 for Agentic Applications, NIST AI RMF, and MITRE ATLAS. Familiarity with MCP, A2A, ISO/IEC 42001, and the EU AI Act. Comfortable mapping deployments to HIPAA, FedRAMP, and SOC 2.\n Fine-grained authorization. ReBAC and ABAC with policy engines (OPA, Cedar, OpenFGA, or equivalent), and a working understanding of how agents acquire tokens, call APIs, and delegate.\n AI hands-on. Built production integrations with Claude, ChatGPT, Microsoft Copilot, Agentforce, Bedrock, LangChain, CrewAI, the OpenAI Agents SDK, or MCP servers.\n AI-native development. Daily use of Claude Code, Cursor, GitHub Copilot, or equivalent.\n Customer-facing range. At home in a customer standup and a CISO briefing on the same day. You build trust with senior engineering leaders and you stay in the room when their internal politics get sharp.\n High agency, founder’s mindset. A zero-to-one self-starter who owns outcomes end to end.\n \n #LI-Remote\n P25","salary_min":200000,"salary_max":275000,"location":"Bellevue, WA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","code-generation","agents","cloud","security"],"apply_url":"https://www.okta.com/company/careers/opportunity/7961356?gh_jid=7961356","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T13:23:38Z","expires_at":"2026-06-29T14:09:00.148157Z","created_at":"2026-05-28T14:10:39.406925Z","updated_at":"2026-05-30T14:09:00.25852Z","company_name":"Okta","company_slug":"okta","company_logo_url":"https://www.google.com/s2/favicons?domain=okta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a1e6623a-57bb-42a8-964e-e2b9698f0e35"},{"id":"afcbd32a-2f1d-4a75-9315-8bcd1e76ad5e","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Fulfillment Planning","slug":"staff-machine-learning-engineer-fulfillment-planning-8c6dac71","description":"About the Team \n The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability.  Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation.\n The team works on some of DoorDash’s most important logistics systems, including:\n \n The core assignment engine that matches deliveries with Dashers in real time.\n Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines.\n Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering.\n ML models and optimization algorithms that shape demand, improve service quality, and reduce cost.\n Tier-0 logistics services that require high reliability, low latency, and strong operational discipline.\n \n The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash.\n About the Role \n We’re looking for a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that drive real-time decisioning across DoorDash’s fulfillment ecosystem.\n You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines.\n This is a high-impact individual contributor role for someone who enjoys building 0→1 ML systems, operating at Staff-level scope, and influencing technical direction across multiple teams. You will define architectures, set modeling and deployment standards, mentor other engineers, and help shape how DoorDash applies machine learning to logistics at scale.\n You’re excited about this opportunity because you will… \n \n Own and build foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.\n Work on challenging, real-world machine learning problems , including real-time assignment, routing, and fulfillment estimation.\n Lead 0→1 ML initiatives , defining how machine learning and optimization are applied across fulfillment products.\n Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.\n Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.\n Establish best practices for model development, deployment, monitoring, retraining, and governance.\n Define and lead DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics\n Mentor other engineers and raise the technical bar for logistics ML across the organization.\n \n We’re excited about you because… \n \n You have 8+ years of industry experience building and deploying production-scale machine learning systems.\n You have strong machine learning fundamentals and know how to apply them to large-scale production systems.\n You are fluent in Python\n You have hands-on experience with modern ML frameworks, especially deep learning frameworks.\n You have designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.\n You can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.\n You communicate clearly with both technical and non-technical audiences.\n You are comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.\n You have built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.\n You have experience with knowledge distillation from large teacher models into efficient production models.\n \n  \n Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only\n We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.\n The Covey tool has been reviewed ","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["llm","fine-tuning","generative-ai","cloud","healthcare","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/7962110","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T23:47:57Z","expires_at":"2026-06-29T14:18:34.57356Z","created_at":"2026-05-28T14:20:10.032116Z","updated_at":"2026-05-30T14:18:34.681457Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/afcbd32a-2f1d-4a75-9315-8bcd1e76ad5e"},{"id":"f18a9045-c453-4617-84c9-63c285e45fbc","company_id":"65bce624-56a0-4c48-8e69-eda588090849","title":"Full Stack Engineer, AgentControl","slug":"full-stack-engineer-agentcontrol-9ca8eb11","description":"About the Job: \n As a Full Stack Engineer on LaunchDarkly’s AgentControl team, you'll build critical systems such as the web application that customers interact with on a daily basis and distributed systems that power AI evaluations at scale. These systems enable customers to control, monitor, and optimize their agent’s functionality. You’ll primarily work in Go, Python, and Typescript and will work directly with other technologies and tools such as AWS, CockroachDB, and Datadog.\n LaunchDarkly’s AgentControl team is on a mission to manage the complete software development lifecycle for shipping agents to production, from configuring to benchmarking to observing and beyond. This team has a huge opportunity ahead of it; we're growing fast and we need your help writing the next chapter in our story.\n Responsibilities:\n \n \n Build and maintain scalable backend services and APIs that power the AI Configs product\n \n Collaborate with internal stakeholders, including product managers, designers, and other LaunchDarkly engineering teams, to understand real-world challenges and improve GenAI workflows using our own platform.\n \n Continuously optimize the performance, reliability, and scalability of our systems\n \n Participate in architecture and design discussions, offering thoughtful input and collaborating with senior engineers to identify tradeoffs and make well-informed decisions\n \n Keep up-to-date with the latest developments in the AI field and in software development best practices\n \n Take ownership of code in production, participate in on-call rotations, and help improve the reliability and maintainability of our systems over time.\n \n Qualifications:\n \n \n 5+ years of professional software engineering experience, with a track record of shipping production-quality full-stack features.\n \n Hands-on experience building GenAI features, Agents, and/or working with Large Language Models (LLMs), either through direct model integration or leveraging AI APIs like OpenAI, Google Cloud AI, or AWS Bedrock.\n \n Understanding of prompt engineering, fine-tuning models, or deploying AI services in production environments.\n \n Experience with designing, implementing, and maintaining RESTful APIs\n \n Experience writing production-ready code with emphasis on quality and maintainability\n \n Experience with distributed systems or data ingestion\n \n Strong computer science fundamentals\n \n Committed to working in a communicative, collaborative environment\n \n Comfortable navigating ambiguous challenges in a rapidly evolving domain, and excited about staying at the forefront of AI advancements\n \n Strong sense of ownership and accountability for delivering impactful solutions\n \n Proven ability to work closely with Product and Design teams to define requirements for new features in a fast-paced iterative environment\n \n \n Pay: \n Target pay ranges based on Geographic Zones* for Level 3: \n \n Zone 1: San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle - $171,200 - $235,400**\n Zone 2: Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago - $154,100 - $211,860**\n Zone 3: All other US locations - $145,500 - $200,090**\n \n LaunchDarkly operates from a place of high trust and transparency; we are happy to state the pay range for our open roles to best align with your needs. Exact compensation may vary based on skills, experience, and location. \n *Within the United States, our geographic pay zones are defined by counties surrounding major metropolitan areas. **Restricted Stock Units (RSUs), health, vision, and dental insurance, and mental health benefits in addition to salary. \n About LaunchDarkly: \n Modern software delivery was supposed to be the foundation for a thriving digital business but reality has proven otherwise. Slow, inefficient development cycles, costly outages, and fragmented customer experiences are preventing developers from building their best software. The LaunchDarkly platform helps developers innovate on new features faster while protecting them with a safety valve to instantly rewind when things go wrong. Developers can target product experiences to any customer segment and maximize the business impact of every feature. And by gradually rolling out new application components, they escape nightmare \"big-bang\" technology migrations. \n The LaunchDarkly platform was built to guide engineers to the next frontier of DevOps by:\n \n Improving the velocity and stability of software releases, without the fear of end customer outages\n Delivering targeted experiences by easily personalizing features to customer cohorts\n Maximizing the business impact of every feature through the ability to experiment and optimize\n Coordinating the release and optimization of software to provide consistent experiences across mobile platforms and device types\n Improving the effectiveness and productivity of engineering teams, by providing insights into engineering cadence and stability\n \n At LaunchDarkly, we b","salary_min":145500,"salary_max":200090,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","llm","fine-tuning","generative-ai","cloud","fullstack"],"apply_url":"https://job-boards.greenhouse.io/launchdarkly/jobs/7750116003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T22:02:02Z","expires_at":"2026-06-29T14:16:49.153499Z","created_at":"2026-05-28T14:18:25.498364Z","updated_at":"2026-05-30T14:16:49.263304Z","company_name":"LaunchDarkly","company_slug":"launchdarkly","company_logo_url":"https://www.google.com/s2/favicons?domain=launchdarkly.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f18a9045-c453-4617-84c9-63c285e45fbc"},{"id":"41b3afd9-e8d0-4d82-9e8e-9149ad7c9147","company_id":"0bedcaf4-210e-4f52-95d5-a82be8aff446","title":"Sr Machine Learning Engineer, AI Research","slug":"sr-machine-learning-engineer-ai-research-866a2680","description":"Join the company that’s building the telemetry infrastructure for the AI era. At Cribl, we partner with IT and Security teams at many of the world’s biggest enterprises, including half of the Fortune 100, to bridge the gap between AI ambition and infrastructure reality. As the AI Platform for Telemetry, we give customers the choice, control, and flexibility to manage and analyze telemetry for both humans and agents, so they can build what’s next.\n We’re one of the fastest‑growing private companies and a leading player in a massive, fast‑moving market. With a global workforce, we’re remote‑first and grounded in a simple idea: software is a people business. Cribl is the place where curious, collaborative people can do their best work, grow fast, and bring their full selves to the herd.\n Why You'll Love This Role \n You will work closely with the founding team and a group of highly-skilled engineers to shape the future of AI-enabled Security/Observability platforms. You will play a central role in bringing integrating cutting-edge AI/ML technologies to the Cribl Product suite to help solve real customer problems.  You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers.\n On top of it all you will have fun. \n Cribl strives to be a great place to work for everyone.\n As An Active Member Of Our Team, You Will... \n \n Design, train, and evaluate machine learning models across a range of research and applied AI initiatives\n Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements\n Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems\n Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation\n Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation\n Contribute to a culture of rigorous experimentation; tracking results, documenting findings, and sharing learnings with the broader team\n Stay current with the latest developments in ML and AI research, and proactively identify opportunities to apply them\n This position may require stand-by, on-call, or off-hours duties during critical research or deployment milestones\n \n If You've Got It - We Want It \n \n Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 4+ years of industry or research experience (Master's or PhD a plus)\n Deep hands-on experience training and evaluating ML models, including language models\n Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow\n Familiarity with MLOps tooling and infrastructure (e.g., MLflow, Weights \u0026 Biases, Kubeflow, or similar)\n Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques\n Strong ability to move fast without sacrificing rigor; you know when to prototype and when to productionize\n Excellent communication skills with the ability to clearly present experimental results to both technical and non-technical stakeholders\n \n #LI-Tag #LI-Remote\n The salary for this role is dependent on geographic location and will be based on the individual candidate's job-related knowledge, skills, and experience. In addition to base salary, for sales and some sales-adjacent roles, employees are eligible to earn incentive compensation (commission). For all other roles, employees are eligible to participate in the Cribl Corporate Bonus Program. In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), and equity.\n Base Salary Range\n $185,000 — $215,000 USD \n Bring Your Whole Self Diversity drives innovation, enables better decisions to support our customers, and inspires change for the better. We’re building a culture where differences are valued and welcomed, and we work together to bring out the best in each other. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which the candidate is applying. \n Interested in joining the Cribl herd? Learn more about the smartest, funniest, most passionate goats you’ll ever meet at cribl.io/about-us .","salary_min":185000,"salary_max":215000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["nlp","tensorflow","computer-vision","mlops","pytorch","reinforcement-learning","fine-tuning","research"],"apply_url":"https://cribl.io/job-detail/?gh_jid=5979543004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T18:02:31Z","expires_at":"2026-06-29T14:18:07.512926Z","created_at":"2026-05-28T14:19:42.491471Z","updated_at":"2026-05-30T14:18:07.623902Z","company_name":"Cribl","company_slug":"cribl","company_logo_url":"https://www.google.com/s2/favicons?domain=cribl.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/41b3afd9-e8d0-4d82-9e8e-9149ad7c9147"},{"id":"8f9c6077-75d0-4b5b-b703-965d816fb18b","company_id":"cf6855a4-6591-475f-be10-f3f36cf31758","title":"Senior Product Manager, AI Agent Orchestration","slug":"senior-product-manager-ai-agent-orchestration-11c09d00","description":"WHAT IS BOX?  \n Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.\n By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.\n WHY BOX NEEDS YOU  \n Box AI is transforming the way businesses manage and leverage content by incorporating cutting-edge artificial intelligence and machine learning capabilities into the Box Content Cloud. As a Senior Product Manager for Box AI Agents , you will play a key role in building the next frontier of intelligent content management, leading AI-driven solutions that revolutionize how enterprises interact with their content.\n In this role, you will be at the helm of driving Box’s AI platform strategy, execution, and iteration, shaping the future of enterprise AI solutions . You’ll define and execute product roadmaps that leverage AI technologies, such as large language models (LLMs) and retrieval-augmented generation (RAG), to unlock insights, automate workflows, and improve collaboration across global enterprises. Your work will directly impact how businesses across the world manage and extract value from their content at scale.\n If you're passionate about defining and leading AI product strategies, bringing AI solutions to market, and driving real-world business impact, this is your chance to lead a high-impact role in the next chapter of Box’s AI growth.\n WHAT YOU'LL DO \n \n Lead the strategy and long-term architecture for Box's core AI orchestration platform — the foundational layer that powers how AI agents reason, plan, and act across the entire Box product surface.\n Define and own a technically complex, mission-critical product area at the intersection of AI infrastructure and enterprise-grade content workflows, balancing the needs of internal platform teams, external developers, and enterprise customers.\n Drive the next generation of agent architecture, anticipating how advances in AI reasoning, multi-step planning, and compute paradigms will reshape what's possible — and translating that vision into a concrete, sequenced roadmap.\n Serve as the connective tissue across every team building on the AI platform — establishing shared primitives, coordinating dependencies, and ensuring that foundational architectural decisions scale gracefully as adoption grows.\n Lead end-to-end product development for the orchestration layer: from identifying capability gaps and architectural constraints to shipping production-grade solutions and iterating based on real-world usage signals.\n Collaborate deeply with engineering and applied AI teams to design and evolve the systems that govern how tools are invoked, how context is managed, and how agents coordinate across complex, multi-turn workflows.\n Establish a rigorous, metrics-driven approach to platform health and capability maturity — defining the right signals to measure reliability, latency, and quality at scale across diverse enterprise use cases.\n Partner cross-functionally with teams across features, infrastructure, and go-to-market to ensure the platform's capabilities are well-understood, well-documented, and effectively leveraged by every team building on top of it.\n Stay at the leading edge of the AI agent landscape — continuously evaluating emerging patterns in orchestration, tool use, and agentic compute — and bringing that perspective to bear on Box's platform strategy.\n \n WHAT YOU'LL DO  \n \n Lead the strategy and long-term architecture for Box's core AI orchestration platform — the foundational layer that powers how AI agents reason, plan, and act across the entire Box product surface.\n Define and own a technically complex, mission-critical product area at the intersection of AI infrastructure and enterprise-grade content workflows, balancing the needs of internal platform teams, external developers, and enterprise customers.\n Drive the next generation of agent architecture, anticipating how advances in AI reasoni","salary_min":192500,"salary_max":240500,"location":"Redwood City, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["rag","embeddings","cloud","fine-tuning","llm","agents"],"apply_url":"https://job-boards.greenhouse.io/boxinc/jobs/7959067","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T16:34:48Z","expires_at":"2026-06-29T14:19:20.680259Z","created_at":"2026-05-28T14:21:03.444277Z","updated_at":"2026-05-30T14:19:20.791946Z","company_name":"Box","company_slug":"box","company_logo_url":"https://www.google.com/s2/favicons?domain=box.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8f9c6077-75d0-4b5b-b703-965d816fb18b"},{"id":"c96f95a6-0aa8-42c2-9fd5-75a8b7173a25","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Principal Machine Learning Engineer- LLM Fine-tuning and Optimization ","slug":"principal-machine-learning-engineer-llm-fine-tuning-and-optimization-bfee7362","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.\n The Community You Will Join:  \n Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb. \n The CS AI product team is responsible for driving CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting the Generative AI technologies to enable an intelligent, scalable and exceptional service experience. The team develops and enhances various AI models, ML services and tools including LLM fine-tuning, alignment and optimization, RAG/Search,  LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. \n What you will do: \n As a principal machine learning engineer, you will be responsible for fine-tuning state-of-the-art LLMs for diverse use cases while optimizing models for high-performance deployment on Airbnb’s ML Infrastructure. You will partner with product managers, software engineers, data scientists and operation teams to brainstorm, design and develop AI products such as AI Assistant, Autonomous agent,  recommendation, travel planning, and many more products that make meaningful impacts in the world of travel. \n Your responsibilities:  \n \n Work with large scale structured and unstructured data; explore, experiment, build and continuously improve foundation models for Airbnb product, business and operational use cases.\n Create a multi-year tech roadmap that enables our team to stay on the leading edge of the rapidly evolving AI landscape and leverage the best in class technologies to deliver customer benefits.\n Continuously evaluate recent and upcoming large foundational models, ensuring the selection and refinement of the highest quality models for enhanced performance and efficiency.\n Hands-on prototype, develop and productionize LLM models and pipelines at scale, including both batch and real-time use cases.\n Drive key AI architectural decisions for products, and contribute to Airbnb’s ML platform architecture and strategy.\n \n Minimum Qualifications :\n \n PhD in Computer Science,  Machine Learning, Mathematics, Statistics, or related technical field.\n 10+ years of experience with developing machine learning models and products at scale from inception to business impact.\n Programming experience in Python and hands-on experience with frameworks such as PyTorch.\n Proven record of training, fine tuning, optimizing models and inference run-time\n Post-training experience in areas like data processing for fine-tuning; responsible LLMs; LLM alignment; reinforcement learning; efficient training and inference; language model evaluation; and/or multilingual and multimodal modeling.\n Or specialized experience in runtime optimizations, model quantization, compression, on-device inference, GPU inference, pytorch, kernel development\n \n Preferred Qualifications: \n \n PhD in AI, machine learning, data science, or related technical fields.\n \n Publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL). \n \n Customer Support Systems : Experience with AI technologies in customer support applications.\n Agile Practice for AI production : Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain.\n \n \n Infrastructure Acumen : Experience deploying and scaling business-critical AI services and driving architectural requirements on ML infrastructures\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity.  Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list . If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. \n Our Commitment To Inclusion \u0026 Belonging: \n Airbnb is committed to working with the broadest talent","salary_min":292000,"salary_max":365000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","fine-tuning","pytorch","generative-ai","llm","payments","agents","machine-learning"],"apply_url":"https://careers.airbnb.com/positions/7955579?gh_jid=7955579","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-24T23:37:28Z","expires_at":"2026-06-29T14:09:02.019884Z","created_at":"2026-05-27T14:09:19.150599Z","updated_at":"2026-05-30T14:09:02.131762Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c96f95a6-0aa8-42c2-9fd5-75a8b7173a25"},{"id":"a7e6a301-28c1-4bf3-b2d5-149f49ee0273","company_id":"9b9ed348-97e6-4bb8-987a-d4db92c3949a","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-084fb6cb","description":"What’s in it for you?  \n Ready to make a serious impact? Millions of people already rely on Calendly, and we’re still in the midst of exciting product growth — it’s a fantastic time to join us. Everything you’ll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you’ve ever worked with, then we hope you’ll consider allowing Calendly to be a part of your professional journey.\n About the team \u0026 opportunity  \n What’s so great about working on Calendly’s Data Science \u0026 Machine Learning team? \n We make things possible for our customers through innovation in data, analytics and AI.\n Why do we need you? Well, we are looking for a Machine Learning Engineer who will deliver business value by building new AI Products for Calendly’s customers. You will report to the head of AI and will be responsible for building and operating AI-powered features that create magical experiences for our customers.\n Our team:\n \n Builds new AI products for Calendly’s customers, bringing new capabilities to life.\n Works closely with product, design, marketing, customer success, and engineering teams to implement AI solutions to real customer problems.\n Drives innovation and makes an impact.\n \n You will join a high performing AI team and be an integral part of building new, AI based experiences for internal and external customers alike.\n What you’ll do \n On a typical day, you’ll own features end to end within our AI ecosystem, with growing independence and impact.\n \n Own AI powered features from design through deployment, partnering with product, design, and engineering to scope work and define success metrics.\n Understand and share domain knowledge, answering domain specific questions for your product area and documenting what you learn for the team.\n Prioritize your work independently, balancing feature development, quality, and maintenance, and communicating tradeoffs clearly.\n Proactively seek and offer support to teammates pairing, reviewing, and collaborating to move projects forward.\n Understand and troubleshoot our deployment pipelines, including build, test, and release steps for ML services and data pipelines.\n Use our monitoring and observability tools to effectively triage alerts and incidents, collaborating with partners to restore service and prevent recurrence, and participate in the team’s on-call rotation and incident response. \n Serve as a subject matter expert for the features and services you own, including their data contracts, SLAs, and dependencies.\n Be a frequent user of AI Tools and champion of adoption to the rest of the company.  \n \n  \n What do we need from you? \n \n 6+ years of industry experience in applied AI/ML and software engineering with a demonstrated track record of shipping and operating AI /ML applications in production.\n Experience working on GenAI products architecture/system design and implementation. \n Hands-on experience implementing AI/ML solutions for high-traffic, low-latency, large-data applications that produced tangible impact for end users.\n Understanding of foundation models and the open-source ecosystem, including model fine-tuning and prompt engineering for real product use cases.\n Strong programming (Python / Scala / Java / SQL etc) and data engineering skills.\n High level of ownership, and the ability to find a way to success. \n You have strong verbal and written communication skills. \n Authorized to work lawfully in the United States of America as Calendly does not engage in immigration sponsorship at this time  \n \n What’s in it for you?  \n Ready to make a serious impact? Millions of people already rely on Calendly’s products, and we’re still in the midst of our growth curve — it’s a fantastic time to join us. Everything you’ll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you’ve ever worked with, then we hope you’ll consider allowing Calendly to be a part of your professional journey. \n Our Hiring Process: \n We aim to provide an inclusive and equitable candidate experience to everyone who expresses interest in working at Calendly. To learn more about our hiring process, please visit our careers page at www.careers.calendly.com .\n Once selected for an opportunity, the recruiter assigned to the role will keep you informed every step of the way. Have questions? Let your recruiter know! Want to share your experience? We are passionately committed to improving and building on our process, and we consider feedback a gift. \n If you are an individual with a disability and would like to request a reasonable accommodation as part of the application or recruiting process, please contact us at recruiting@calendly.com .  \n Calendly is registered as an employer in many, but not all, states. If you are located in Alaska, Alabama, Delaware, Hawaii","salary_min":198025,"salary_max":239960,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","data-pipeline","generative-ai","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/calendly/jobs/8563197002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T22:58:30Z","expires_at":"2026-06-29T14:18:10.748008Z","created_at":"2026-05-27T14:19:02.542405Z","updated_at":"2026-05-30T14:18:10.860952Z","company_name":"Calendly","company_slug":"calendly","company_logo_url":"https://www.google.com/s2/favicons?domain=calendly.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a7e6a301-28c1-4bf3-b2d5-149f49ee0273"},{"id":"d134135d-62d9-4aa9-acb7-410bbd77911c","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Sr. Staff Machine Learning Engineer, Content Ecosystem","slug":"sr-staff-machine-learning-engineer-content-ecosystem-3ffaf377","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Pinterest works when the content ecosystem works: when people can reliably find ideas that feel inspiring, trustworthy, and actionable—and when the ecosystem continuously learns what to create, surface, and sustain next. In this Sr. Staff ML Engineer role, you’ll be the technical lead shaping how Pinterest understands and improves its content as a living marketplace: a dynamic system with feedback loops between users, creators/publishers, distribution, and long-term business outcomes.\n You will define a durable ML strategy that goes beyond “engagement metrics” to improve overall ecosystem health—identifying where we’re underserving content, uncovering the attributes that make content succeed, and designing optimization approaches that balance relevance, quality, diversity, integrity, and monetization. The problems are inherently multi-objective and long-horizon: the best decisions today should strengthen the ecosystem tomorrow. If you’re excited by high-leverage technical leadership, rigorous ML thinking, and marketplace-style dynamics at scale, this role offers a chance to directly shape Pinterest’s moat and the experience millions of people come to for ideas they can act on.\n What you’ll do: \n \n Set technical strategy and vision for ML systems that improve the end-to-end content ecosystem, including supply, distribution, and engagement/utility outcomes.\n Partner with DS teams to develop a content ecosystem measurement framework to quantify content health and performance (e.g., content quality, freshness, diversity, coverage, creator/content sustainability, and user value), and align it with company/business goals.\n Identify and close content gaps by building models and insights that answer: what content is missing, for whom, in which contexts, and why.\n Deeply understand what content works and why by combining causal thinking, experimentation, and model interpretability to connect content attributes and distribution mechanisms to downstream user and business outcomes.\n Build and optimize content marketplace mechanisms that balance multi-sided incentives and constraints (e.g., users, creators/publishers, advertisers, internal policy/safety), while maximizing long-term ecosystem value.\n Design multi-objective optimization approaches that manage tradeoffs across relevance, quality, diversity, creator incentives, integrity/safety, and monetization.\n Partner closely with cross-functional teams (Product, Data Science, UX Research, Content/Creator teams, Trust \u0026 Safety, Ads, Infra) to translate ambiguous ecosystem problems into clear technical roadmaps and deliver measurable impact.\n Mentor and grow junior ML engineers through technical coaching, design reviews, career development support, and creating a culture of strong engineering and scientific rigor.\n Raise the quality bar for ML engineering by establishing best practices for data quality, model governance, reliability, privacy-aware design, and operational excellence.\n Communicate clearly and influence broadly by producing crisp technical proposals, aligning stakeholders on tradeoffs, and driving decisions across org boundaries.\n Explore and apply advanced methods where beneficial—e.g., game-theoretic approaches, reinforcement learning, mechanism design, or bandit-style optimization—to improve marketplace dynamics and long-term ecosystem outcomes.\n \n What we’re looking for: \n \n Strong fundamentals in machine learning and optimization, with the ability to apply them to real-world, high-scale ecosystem problems.\n Demonstrated ability to lead technical strategy, navigate ambiguity, and deliver end-to-end impact.\n Deep interest in marketplace dynamics (multi-sided incentives, feedback loops, long-term health metrics), and comfort with multi-objective tradeoffs.\n Experience with Cursor, Copilot, Codex, or","salary_min":227871,"salary_max":469147,"location":"San Francisco, CA","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","code-generation","reinforcement-learning","llm","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=7919043","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T21:44:36Z","expires_at":"2026-06-29T14:08:27.825754Z","created_at":"2026-05-27T14:08:41.489689Z","updated_at":"2026-05-30T14:08:27.939307Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d134135d-62d9-4aa9-acb7-410bbd77911c"},{"id":"4ec47e11-41e8-4449-b012-74d40f99df46","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Ads Conversion","slug":"staff-machine-learning-engineer-ads-conversion-06f85a08","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n We are looking for a Staff MLE to lead the technical vision for our Ads Advanced Conversion Modeling team, building the state-of-the-art systems that power our global marketplace.\n  \n What you’ll do:  \n \n Lead the technical direction and development of state-of-the-art applied ML projects for ads conversion.  \n Design and build large-scale DNN models to improve user action prediction with low latency.  \n Mine text, visual, and user signals to better understand intention and infer interests from online activity.  \n Use AI to accelerate analysis and iteration, while applying judgment and verification to ensure correctness and quality.  \n Automate repeatable tasks such as documentation, reporting, and QA checks to speed up the development lifecycle.  \n Coach and mentor engineers while collaborating with product and sales to design new ad products.\n \n  \n What we’re looking for: \n \n Degree in Computer Science, Statistics, or a related field.  \n 6+ years of industry experience building production ML systems at scale (Search, Recommendations, or Ranking).  \n 2+ years of experience leading technical projects or teams.  \n Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.  \n Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.\n Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.\n High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final deliverables.\n Strong mathematical foundation and experience with statistical methods and A/B testing. \n \n  \n Relocation Statement:  \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.\n This role will need to be in the office for in-person collaboration 1-2 times every 6 months and therefore can be situated anywhere in the country.\n \n  \n #LI-SM4\n #LI-REMOTE\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.\n Information regarding the culture at Pinterest and benefits available for this position can be found here . \n US based applicants only\n $222,716 — $389,753 USD \n Our Commitment to Inclusion: \n Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete  this form  fo","salary_min":222716,"salary_max":389753,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","llm","code-generation","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=7902034","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T19:35:37Z","expires_at":"2026-06-29T14:08:28.182671Z","created_at":"2026-05-27T14:08:41.839976Z","updated_at":"2026-05-30T14:08:28.299621Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4ec47e11-41e8-4449-b012-74d40f99df46"},{"id":"1598afd6-7ff3-417e-b3a0-138ecb576a46","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Machine Learning Scientist","slug":"machine-learning-scientist-de3cf41b","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n WHY DATA SCIENCE AND ANALYTICS \n The Data Science \u0026 Analytics organization's mission is to increase our speed, frequency, and acumen in making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum, including analytical data engineering, product analytics, experimentation, causal inference, statistical modeling, and machine learning. Aligned and partnering with product verticals, we use this extensive tool belt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products, and measure impact on our community of players and developers.\n In Data Science \u0026 Analytics, you will contribute to horizontal ML systems and infrastructure that enable us to understand the trajectory of users and creators as well as the overall business to inform investment opportunities and accelerate our growth. These systems inform experiment decision making, product roadmaps, and execution risk on our road to connecting one billion users. \n You Will:  \n \n Develop, build, and support large scale forecasting systems for business growth\n Develop, build, and support modeling of long-term user outcomes\n Design and implement batch prediction infrastructure that ensures high-levels of accuracy, provides explainability, and quantifies uncertainty\n Collaborate with data science and product partners to unlock causal understanding of our business growth and to develop scalable solution for measuring ecosystem health\n Model and promote a high bar for technical excellence in the broader data science and ML community.\n Communicate strategic findings to influence company and team-level roadmaps\n Partner with Data Engineering and Data Platform teams to ensure model development, reporting, and monitoring systems are built in a reliable and robust way. \n \n You Have:  \n \n 5+ years of industry experience in prototyping and building scalable machine learning solutions.\n Experience building scalable and robust ETL data and ML pipelines with complex upstream dependencies and accountability for downstream consumers\n Experience with time series modeling in practice and theory. Experience with foundational time series models (i.e. transformer based) and fine tuning frameworks.\n Demonstrated ability to lead project areas from scratch, and break product requirements into iterative deliverable stages.\n Strong communication skills to connect model outputs to company-level strategy and to integrate horizontal solutions into vertical team operations and systems.\n An Advanced Degree (MSc or PhD) or equivalent degree in Statistics, Economics, Operations Research, Computer Science, Applied Math, Physics, Engineering, or other quantitative fields.\n For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page .\n Annual Salary Range\n $263,670 — $322,820 USD \n Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).\n Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.\n For US based roles only, please note the Company may not be able to employ candidates for this role who have United","salary_min":263670,"salary_max":322820,"location":"San Mateo, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","data-pipeline","machine-learning","data-science"],"apply_url":"https://careers.roblox.com/jobs/7950872?gh_jid=7950872","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T03:02:22Z","expires_at":"2026-06-29T14:17:02.317307Z","created_at":"2026-05-27T14:17:50.446567Z","updated_at":"2026-05-30T14:17:02.42643Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1598afd6-7ff3-417e-b3a0-138ecb576a46"},{"id":"3a790011-3259-4ddc-b03a-1e3227951d9b","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Forward Deployed Engineer","slug":"forward-deployed-engineer-988ebd0a","description":"Who We Are \n Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.\n Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.\n We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.\n  \n What We Are Looking For \n We are seeking an experienced  Forward Deployed Engineer  to partner directly with customers to architect, build, and deploy production AI systems and workflows on Lightning AI’s platform. In this role, you will own the customer journey from early exploration through production deployment, translating ambiguous business goals into reliable, observable systems with clear quality, latency, scalability, and cost outcomes.\n This role sits at the intersection of software engineering, research engineering, AI infrastructure, product thinking, and customer engagement. You’ll work closely with customer engineering teams as well as Lightning’s internal product and engineering organizations to deliver production-ready AI systems that help customers realize value quickly and scale with confidence.\n This is a hands-on engineering role that combines software development, AI infrastructure, technical customer engagement, and product thinking. Successful candidates will be  highly technical, customer-oriented builders who thrive in fast-moving environments and enjoy solving ambiguous, real-world AI systems problems.\n This role is based in one of our hubs (New York City, San Francisco, Seattle, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. \n What You'll Do \n \n Partner directly with customers to design, implement, and deploy end-to-end AI systems and workflows on Lightning’s platform\n Translate vague customer objectives into clear technical specifications, proof-of-concepts, and scalable production implementations\n Own customer technical engagements end-to-end, from early discovery and architecture through deployment, monitoring, and expansion\n Develop and maintain production-grade software systems and services using modern programming languages, with a strong preference for Python\n Build reliable, observable systems with strong attention to latency, throughput, quality, scalability, and cost efficiency in production environments\n Debug and optimize AI systems across inference infrastructure, model behavior, APIs, and distributed workloads to improve performance and reliability\n Work closely with customer engineering teams throughout the full lifecycle of AI deployments, including technical discovery, implementation, deployment, and scaling\n Collaborate cross-functionally with Lightning’s product and engineering teams to improve platform capabilities, influence roadmap priorities, and identify opportunities for reusable product improvements\n Navigate ambiguity with sound technical judgment, making thoughtful tradeoffs and selecting the right tools and approaches without introducing unnecessary complexity\n Demonstrate strong ownership and accountability in execution, with a commitment to delivering high-quality outcomes for both customers and internal teams\n \n What You’ll Need \n Required Qualifications \n \n Strong software engineering experience building and maintaining production systems in one or more general-purpose programming languages, with Python strongly preferred\n Experience working directly with customers in highly technical environments, such as Forward Deployed Engineering, Solutions Engineering, Applied AI Engineering, Technical Product Engineering, or related roles\n Familiarity with AI/ML pipelines and the lifecycle of model development, evaluation, deployment, and monitoring\n Experience deploying and operating production AI/ML systems in cloud or distributed environments\n Familiarity with modern AI infrastructure and tooling such as Docker, Kubernetes, APIs, model serving systems, or distributed inference workloads\n Strong communication and collaboration skills, especially when working through complex technical topics with customers, engineers, and cross-functional stakeholders\n Ability to translate business needs into technical solutions and drive projects from initial concept through production delivery\n Ability to execute effectively in ambiguous, fast-moving, high-growth environments\n Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field\n \n Nice-to-Haves \n \n Experience building, deploying, or optimizing large-scale AI/ML","salary_min":120000,"salary_max":250000,"location":"London, UK","workplace":"hybrid","job_type":"full-time","experience_level":"mid","tags":["distributed-systems","fine-tuning","pytorch","embeddings","search","llm","mlops"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/7742081003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T17:15:55Z","expires_at":"2026-06-29T14:03:02.726355Z","created_at":"2026-05-27T14:03:14.78242Z","updated_at":"2026-05-30T14:03:02.834329Z","company_name":"Lightning AI","company_slug":"lightning-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=lightning.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3a790011-3259-4ddc-b03a-1e3227951d9b"}],"page":1,"per_page":20,"total":827,"total_pages":42}