{"has_next":true,"jobs":[{"id":"f47b2b52-9138-4056-a197-783873a96c39","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Voice Model","slug":"member-of-technical-staff-voice-model-5b5f6cb9","description":"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":"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":"8b3dbb78-3093-481e-9b0c-09e3ed1deb6e","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Principal Software Engineer, Data","slug":"principal-software-engineer-data-0cdb1bea","description":"Your Impact at LILA \n Join us in shaping the future of science! We are seeking Principal Software Engineers with backend experience to join our Data Platform Team (Data), where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!\n About The Team \n The Data Platform Team (Data) builds and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.\n What You'll Be Building \n \n Design \u0026 Build APIs: Design and build high-performance, secure, and well-documented APIs that integrate with AI-driven applications.\n Database Architecture \u0026 Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.\n Performance \u0026 Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.\n Cloud \u0026 Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.\n Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.\n \n What You'll Need To Succeed \n \n Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.\n 8-15 years of engineering experience building and deploying large-scale systems in production. You must be strong in scalable backend system design.\n Full Stack Development : Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)\n Experience with ORMs: Experience with and web services for CRUD services (SQL Alchemy, SQLModel, FastAPI, Django).\n Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).\n Familiarity with Python for Science : Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).\n Hands on experience using AI coding assistants to drive productivity is required.\n Communication \u0026 Collaboration : Acute listening and writing skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.\n Problem Solving : Proven ability to design complex backend solutions, balancing trade-offs between scalability, performance, and maintainability.\n \n Bonus Points For \n \n Cloud \u0026 DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).\n Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.\n Experience with laboratory devices, robotics, or hardware\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $204,000 — $348,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials","salary_min":204000,"salary_max":348000,"location":"Boston, MA","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["cloud","pytorch","embeddings","robotics","data-pipeline"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4250071009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T19:39:54Z","expires_at":"2026-06-29T14:17:40.451966Z","created_at":"2026-05-30T14:17:40.562155Z","updated_at":"2026-05-30T14:17:40.562155Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8b3dbb78-3093-481e-9b0c-09e3ed1deb6e"},{"id":"f8a29ca5-91b9-4ad7-9236-f57043203a4b","company_id":"ff51c80a-dce9-4cb4-b2e6-9c060d25ef55","title":"Robotics Engineer, Technical Lead","slug":"robotics-engineer-technical-lead-f0a6b32f","description":"About Applied Intuition\n Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co .\n We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. \n About the role\n This is a founding technical role helping build and shape Applied Intuition’s robotics organization from the ground up. You will set the technical direction for robotics software and AI, write production code, and build functional demos on physical hardware from day one. The role is weighted heavily toward software and learned behaviors, with the expectation that you can engage meaningfully across the hardware stack when needed.\n At Applied Intuition, you will:\n \n Define and own the technical architecture for humanoid robotics software, spanning perception, planning, control, and learned behaviors\n Write production-quality code in Python and C++ and ship it to physical robots — this is a hands-on individual contributor role first\n Design, train, evaluate, and deploy learning-based policies for manipulation and locomotion\n Build functional demonstrations on multiple robot hardware platforms that prove out capabilities and inform the product roadmap\n Establish simulation infrastructure and validate behaviors in physics-based environments before deploying to hardware\n Instrument robots, analyze telemetry and failure data, and iterate quickly to improve robustness in real-world conditions\n Work with teleoperation and data collection pipelines to generate training data and close the sim-to-real gap\n Identify and recruit the next engineers on the team\n \n We're looking for someone who has:\n \n BS, MS, or PhD in Robotics, Computer Science, Electrical Engineering, or a related field, or equivalent hands-on experience\n 7+ years of experience in robotics software development, with a meaningful portion on physical humanoid, legged, or highly dexterous manipulation platforms\n Proven track record shipping learning-based systems — behavior cloning, RL, or VLA policies — to real robots in production or near-production settings\n Strong proficiency in Python and C++ for robotics and ML systems; experience with PyTorch or equivalent deep learning frameworks\n Deep understanding of robotics fundamentals: kinematics, dynamics, control theory, state estimation, and perception\n Experience building and evaluating visuomotor or multimodal policies end to end, from data collection through deployment\n Ability to operate independently in an early-stage environment, make architectural decisions with limited information, and build from scratch\n Comfort on the lab floor — debugging physical robots, running hardware-in-the-loop tests, and iterating on live systems\n \n Nice to have:\n \n Experience with state-of-the-art bi-dexterous mobile hardware platforms, including dexterous manipulation and whole body control\n Familiarity with hardware bring-up, sensor integration, or embedded systems; ability to engage with mechanical and electrical teams at a subsystem level\n Background in SLAM, 3D perception, or sensor fusion (IMU, lidar, cameras, force/torque)\n Experience with physics simulators such as MuJoCo, NVIDIA Isaac Sim, or Gazebo\n Familiarity with ROS/ROS2 or similar robotics middleware\n Publication record or open-source contributions in robot learning, embodied AI, or manipulation\n \n Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.\n Applied Intuition pay ranges reflect the ","salary_min":250000,"salary_max":400000,"location":"Sunnyvale, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["cloud","robotics","deep-learning","pytorch"],"apply_url":"https://boards.greenhouse.io/appliedintuition/jobs/4700425005?gh_jid=4700425005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T17:33:51Z","expires_at":"2026-06-29T14:03:36.517513Z","created_at":"2026-05-30T14:03:36.628511Z","updated_at":"2026-05-30T14:03:36.628511Z","company_name":"Applied Intuition","company_slug":"applied-intuition","company_logo_url":"https://www.google.com/s2/favicons?domain=appliedintuition.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f8a29ca5-91b9-4ad7-9236-f57043203a4b"},{"id":"530f705a-007a-497f-9f62-9a6e196ea9ad","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-e1742de5","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is a remote position based in Toronto, Canada. \n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $210,070 to $282,851 (CAD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and accountability, we enable trust and autonomy—leaders take accountability for decisive action, and teams own outcomes ","salary_min":210070,"salary_max":282851,"location":"Toronto, Canada","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","agents","mlops","gpu","healthcare","deep-learning","pytorch","llm"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961536","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:14Z","expires_at":"2026-06-29T14:07:04.607932Z","created_at":"2026-05-30T14:07:04.722791Z","updated_at":"2026-05-30T14:07:04.722791Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/530f705a-007a-497f-9f62-9a6e196ea9ad"},{"id":"58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-288c8ba8","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is an office-based, hybrid position at our US headquarters located in Salt Lake City, Utah . Employees are expected to work in the office at least 50% of the time.\n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $151,130 to $203,490 (USD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and acco","salary_min":151130,"salary_max":203490,"location":"Salt Lake City, Utah","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pytorch","deep-learning","cloud","mlops","gpu","llm","distributed-systems","healthcare"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961460","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:13Z","expires_at":"2026-06-29T14:07:04.532978Z","created_at":"2026-05-30T14:07:04.642889Z","updated_at":"2026-05-30T14:07:04.642889Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9"},{"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":"88b5244c-2383-4f06-b5ef-0ade11296098","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Technical Lead Manager, Prediction \u0026 Planning, Machine Learning Eval","slug":"staff-technical-lead-manager-prediction-planning-ml-eval-29b43259","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently. \n We have an exciting opportunity for a Staff Technical Lead Manager to lead our ML Evaluation team. In this role, you will define the strategic vision for our evaluation platforms, scaling the critical infrastructure and metrics required, and partner closely with the modeling teams to rigorously validate our next-generation deep neural networks and accelerate ML developer velocity across PrePlan.\n You will: \n \n Influence the strategic direction of foundational infrastructure and evaluation platforms to robustly support next-generation ML model evaluation use cases\n Collaborate cross-functionally with ML engineers, data scientists, and infrastructure teams to identify, define, and surface critical signals on model, component, and system-level performance\n Leverage and scale evaluation and infrastructure platforms to significantly enhance the ML developer experience, enabling faster iteration through earlier, more reliable, and trusted model evaluation\n Manage and mentor a focused team of engineers, aligning their career growth and aspirations with critical organizational needs\n Drive best practices and leverage deep technical awareness of the Alphabet ML stack (e.g., TensorFlow, JAX, Flax, Apache Beam) to optimize evaluation workflows\n Stay at the forefront of emerging technologies, industry trends, and research in ML evaluation methodologies and advanced metrics design\n \n You have:  \n \n M.S. in Computer Science, Mathematics, or equivalent industry experience in Robotics or large-scale ML systems with critical evaluation needs\n 5+ years of experience building and maintaining large-scale distributed infrastructure, ML inference systems, or evaluation platforms, including 3+ years of engineering management experience\n Strong coding and testing proficiency, specifically in Python and C++\n Strong foundational knowledge of model evaluation and core data science principles (e.g., confidence intervals, outlier identification, curve fitting, and causality analysis)\n Familiarity with large-scale ML deployment and orchestration tools (e.g., TF Serving, TorchServe, Kubeflow, SageMaker Pipelines, or Vertex AI Pipelines)\n Understanding of machine learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or TensorFlow\n \n We prefer: \n \n Experience developing and maintaining evaluation pipelines for ML models\n Experience deploying and supporting machine learning models for computer vision, natural language processing, robotics/motion planning, or recommendation systems\n Experience supporting a small team of MLEs developing high-capacity, production-grade models and components\n Strong understanding of metrics computation and regression detection at scale\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["robotics","nlp","computer-vision","pytorch","autonomous-vehicles","deep-learning","tensorflow","evaluation"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7963516","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T17:26:50Z","expires_at":"2026-06-29T14:04:32.30248Z","created_at":"2026-05-29T14:12:24.077985Z","updated_at":"2026-05-30T14:04:32.417838Z","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/88b5244c-2383-4f06-b5ef-0ade11296098"},{"id":"6dc81f39-064c-435f-95c1-b6c70be6a1c5","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Machine Learning Engineer, App SW","slug":"machine-learning-engineer-app-sw-73eaf56f","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The Role  \n As an  ML Engineer  within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalisation, comfort, and collaboration.\n You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.\n Responsibilities:\n \n Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.\n Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.\n Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.\n Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.\n Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.\n Collaborate cross-functionally across various teams to ensure integration and iteration velocity.\n Mentor senior engineers and shape the long-term technical direction across Autonomy.\n \n About you:  \n In order to set you up for success as a Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n Essential\n \n Extensive and proven track record of shipping deep learning systems to production.\n Expert in deep learning (esp. sequential models, control, planning, or perception).\n Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.\n Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.\n Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.\n \n Desirable\n \n Prior work in autonomous driving, imitation learning, or trajectory prediction.\n Familiarity with personalization, human behavior modeling, or driver intent inference.\n Experience integrating ML systems into production hardware or multi-agent simulation.\n \n This role is a full-time role based in Sunnyvale or Detroit (hybrid) and the reasonably estimated salary for this role ranges from $283,500 to $381,600, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.\n At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.  \n #LI-KM1 \n Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. \n We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition  (including breastfeeding) or any other ba","salary_min":283500,"salary_max":381600,"location":"Detroit","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["autonomous-vehicles","data-pipeline","robotics","pytorch","generative-ai","deep-learning","gpu","agents"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8568694002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T14:51:17Z","expires_at":"2026-06-29T14:12:44.75073Z","created_at":"2026-05-29T14:50:38.39389Z","updated_at":"2026-05-30T14:12:44.866872Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6dc81f39-064c-435f-95c1-b6c70be6a1c5"},{"id":"bde15e9d-9623-47f7-a4e3-030d63ab1186","company_id":"57a9b50d-a69a-4f6f-9acb-910495c3c359","title":"Head of Marketing Operations","slug":"head-of-marketing-operations-0e090c3d","description":"About Us: \n At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n About This Role \n The Head of Marketing Operations builds the infrastructure that lets Fireworks marketing scale, and runs the operating system that keeps the team performing day to day. You own the marketing tech stack, the data model, the lifecycle, and the analytics that turn marketing into a predictable pipeline engine that the executive team can trust. You also own the planning rhythms, budget, prioritization, and program management that keep every function inside marketing shipping on time and in sync.\n The right person is rigorous, opinionated about tooling, and energized by the operational problems most marketers avoid. You think about marketing the way a great operator thinks about a business: cadence, accountability, resource allocation, and clear measurement.\n Reports to: SVP Marketing Location: Remote (US) with periodic travel to San Mateo HQ Compensation: Competitive salary + equity\n Location and Work Style \n This role is remote-friendly within the US. You will travel to our San Mateo HQ periodically for team onsites, planning sessions, and key moments that benefit from being in person. We will establish a cadence that works for the team and the role.\n Responsibilities \n Marketing Technology Stack and Architecture \n You own the marketing tech stack end-to-end: selection, implementation, integration, and the standards that govern how data flows between systems. This includes the marketing automation platform, CDP or warehouse-native architecture decisions, enrichment, and the integration layer with Salesforce. Success is measured by stack reliability, total cost of ownership, and the speed at which marketing can launch new programs.\n Marketing Operating System and Program Management \n You run the operating rhythm of the marketing team. This includes the annual and quarterly planning process, goal setting and tracking, weekly business reviews, and cross-functional program management across demand gen, product marketing, content, and brand. You own the marketing budget model, vendor contracts, headcount planning support, and the prioritization framework that turns a long list of ideas into a focused roadmap. You are the connective tissue that makes the rest of the marketing team faster, more aligned, and easier to scale. Success is measured by on-time program delivery, budget accuracy, and team velocity.\n Lifecycle, Lead Management, and Scoring \n You own the full lifecycle from anonymous visitor through closed-won, including lead scoring, MQL and PQL definitions, SLA enforcement, and the handoff to sales. This includes the operational rigor around routing, nurture, and re-engagement. Success is measured by SDR conversion lift and clean handoff metrics.\n Attribution, Reporting, and Analytics \n You own marketing analytics, attribution methodology, and the dashboards that the executive team and the board see. This includes pipeline attribution, channel ROI, and the quarterly marketing performance review. Success is measured by leadership confidence in the numbers and by speed of decision-making informed by them.\n Data Governance and Compliance \n You own data hygiene, privacy compliance (GDPR, CCPA, and emerging US state laws), and the governance model that keeps our database trustworthy as we scale. Success is measured by data quality scores, deliverability rates, and zero material compliance incidents.\n What Success Looks Like \n \n Marketing-sourced pipeline is reported with confidence and audit-ready definitions\n Lead scoring drives measurable lift in downstream conversion rates\n The full funnel from visitor to closed-won is instrumented and visible in shared dashboards\n Campaign launch time drops as a result of better operational playbooks\n Marketing and sales agree on the data, the definitions, and the single source of truth\n The marketing team operates on a clear quarterly cadence with shared priorities, transparent budget, and on-time delivery against the plan\n The SVP of Marketing and the broader exec team have real-time visibility into team capacity, program status, and spend without chasing updates\n \n What This Role Does Not Own \n \n Campaign creative and execution: Demand Gen leader\n Sales technology and territory design: Sales Operations\n Product analytics and PLG instrumentation: Product and Data teams\n \n You Should Have \n \n 8+ years in marketing operations with ","salary_min":250000,"salary_max":280000,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["agents","mlops","generative-ai","llm","pytorch"],"apply_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4260883009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T22:28:00Z","expires_at":"2026-06-29T14:01:53.26087Z","created_at":"2026-05-28T14:02:31.996873Z","updated_at":"2026-05-30T14:01:53.369572Z","company_name":"Fireworks AI","company_slug":"fireworks-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=fireworks.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bde15e9d-9623-47f7-a4e3-030d63ab1186"},{"id":"2f82717a-ca5c-44ec-afbc-871db9888784","company_id":"f36ec848-cb19-4b95-a680-6733e58086c0","title":"Director, Data Science","slug":"director-data-science-e0c2bfe0","description":"May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think. Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We’re building the world’s best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we’ve given more than 500,000 autonomous rides to real people around the globe. And we’re just getting started. We’re hiring people who share our passion for building the future, today, solving real-world problems and seeing the impact of their work. Join us. \n Job Summary \n May Mobility is entering an exciting phase of growth as we expand our autonomous transit and mobility services across the country. Founded in 2017 by a team of experienced roboticists, perception, behavior, AI, and software engineers, we operate driverless transit shuttles in real communities — not as a research demonstration, but as a daily-service product that people rely on to get to work, school, and home.\n The Director, Data Science will lead the team responsible for turning the data generated by our fleet, simulation environment, and ML systems into the insights, evaluations, and decisions that make our autonomous service safer, more efficient, and ready to scale into new cities. You will own data science across simulation and synthetic data, perception and planning ML evaluation, fleet operations analytics, and the data infrastructure that supports them. You will partner directly with Engineering, Product, Operations, and Safety leadership to set measurement standards, define release criteria, and translate frontline operating data into the next generation of our autonomy stack.\n This is a leadership role for someone who has scaled a data science function inside a hard-tech environment, who is comfortable making engineering and product tradeoffs alongside their team, and who sees the gap between research-grade ML and production transit-grade ML as the most interesting problem in the industry today.\n Essential Responsibilities \n \n Set and own the data science strategy across simulation and synthetic data, ML evaluation (perception, prediction, planning), fleet operations analytics, and the data platform that supports them; translate that strategy into a 12–24 month roadmap with measurable milestones.\n Lead, grow, and develop a team of senior data scientists, ML engineers, and front-line managers; recruit from a small expert pool, calibrate the bar, and build a hiring brand that allows May Mobility to win against AV, robotics, and AI competitors.\n Partner with Engineering, Product, Safety, and Operations leaders to define release criteria, performance metrics, and ODD-expansion gates; use data to make the business case for what we deploy, where, and when.\n Drive ML and analytics applications end-to-end: dataset curation, scenario coverage, modeling, offboard evaluation, productionization, and continuous monitoring of fleet performance in the wild.\n Establish measurement and experimentation standards across the company — including before/after analyses for stack changes, A/B-style comparisons in simulation, and statistically credible reporting on real-world incidents.\n Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for production data science systems.\n Track and trend technical performance of the autonomy stack in the field; surface root causes, prioritize fixes with engineering, and represent fleet-data findings to executives, regulators, and partners.\n Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the ML changes most likely to move our key safety and service metrics.\n Represent May Mobility's data science work externally where appropriate — through publications, conference talks, partner reviews, and recruiting.\n \n Skills and Abilities \n Success in this role typically requires the following competencies: \n \n Autonomy Data Expertise. Can reason fluently about the data produced by a modern AV stack — sensor logs, perception outputs, planning traces, simulator results, and operational telemetry — and can identify which signals matter for which decisions.\n Hands-On Technical Depth. Has personally shipped production ML or analytics systems within the last 3–5 years and is credible in code review and design review with senior engineers and scientists.\n Cross-Functional Translator. Can explain a complex ML or statistical finding to engineering, product, and executive audiences; and ","salary_min":217000,"salary_max":312000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["robotics","healthcare","distributed-systems","pytorch","computer-vision","tensorflow","reinforcement-learning","autonomous-vehicles"],"apply_url":"https://job-boards.greenhouse.io/maymobility/jobs/8561428002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T21:54:47Z","expires_at":"2026-06-29T14:17:06.3175Z","created_at":"2026-05-28T14:18:43.046233Z","updated_at":"2026-05-30T14:17:06.431533Z","company_name":"May Mobility","company_slug":"may-mobility","company_logo_url":"https://www.google.com/s2/favicons?domain=maymobility.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2f82717a-ca5c-44ec-afbc-871db9888784"},{"id":"23d134c7-f2bf-4c83-87f8-3938851bc707","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-583097ba","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n ABOUT THE TEAM\n Anduril's Air \u0026 Missile Defense Radar team develops cutting-edge tracking algorithms and software systems that detect, track, and characterize airborne threats in real-time. We're building the next generation of tracking intelligence capabilities—automated analysis systems that understand tracking performance, identify failure modes, and continuously improve our algorithms through data-driven insights.\n This role sits at the intersection of ML engineering and tracking domain expertise. You'll build end-to-end pipelines that ingest tracking algorithm telemetry, analyze correlation failures and performance anomalies, train models to automate root cause analysis, and deploy production tools that help engineers ask questions like \"why didn't track X and track Y associate?\" We don't just track targets; we track our tracking systems and make them smarter.\n WHAT YOU'LL DO\n \n Own tracking intelligence infrastructure end-to-end : Build the platform for ingesting tracking algorithm telemetry (hypotheses, scores, gains, association decisions), feature engineering performance metrics, training analysis models, and deploying them into production\n Automate tracking analysis : Develop ML models that identify correlation failures, track quality degradation, and root causes for tracking anomalies—replacing manual deep-dive investigations with scalable automated insights\n Build autotuning capabilities : Create systems that recognize incoming data characteristics and automatically adjust tracking algorithm parameters, frame rates, and model configurations for optimal performance\n Design human-in-the-loop tools : Build interfaces and query services that let engineers ask natural questions about tracking behavior and get data-driven answers backed by your models\n Exploit tracking telemetry : Instrument C++ tracking algorithms with appropriate logging (working with platform engineers), then marshal that data into consistent formats for analysis and model training\n Deploy in constrained environments : Package and deploy models for air-gapped systems with no external connectivity, following security scanning requirements where ML models are treated as data artifacts\n Manage the ML lifecycle : Handle data catalogs, ground truth labeling, model registries, versioning, and validation—ensuring models improve tracking performance in measurable ways\n Bridge domains : Translate between tracking algorithm fundamentals (Kalman filters, data association, multi-hypothesis tracking) and ML/data science techniques to build solutions that actually work\n Drive make/build decisions : Evaluate when to build custom models vs. leverage existing ML capabilities, selecting appropriate algorithm architectures for tracking intelligence problems\n Work hands-on-keyboard : This is a one-person show initially—you'll architect, code, deploy, and iterate rapidly using modern Python-based ML tooling\n \n REQUIRED QUALIFICATIONS\n \n 3+ years of experience with a strong mix of ML engineering and data science—you've built models AND deployed them into production systems\n Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)\n Experience with MLOps practices: data pipelines, feature engineering, model versioning, experiment tracking, and deployment workflows\n Familiarity with ML infrastructure tooling (MLflow, Dagster/Airflow, or similar orchestration tools)\n Understanding of tracking, estimation, or filtering algorithms (Kalman filters, data association techniques)—you need to understand what tracking algorithms output and why they make the decisions they do\n Ability to work with streaming time-series data and engineer features from algorithm telemetry\n Experience building data catalogs, managing ground truth labels, and validating model performance\n Strong software engineering fundamentals—you can build maintainable, production-quality code independently\n Comfortable working in C++ environments enough to add instrumentation/logging (no deep algorithm development required)\n Ability to obtain and maintain a U.S. Top Secret SCI security clearance\n \n PREFERRED QUALIFICATIONS\n \n Experience deploying ML models in edge, embedded, or air-gapped environments with security constraints\n Background in def","salary_min":165000,"salary_max":218000,"location":"Fort Collins, CO","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["tensorflow","mlops","computer-vision","data-pipeline","pytorch","payments","machine-learning"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5126634007?gh_jid=5126634007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T21:29:38Z","expires_at":"2026-06-29T14:06:48.665653Z","created_at":"2026-05-28T14:08:23.033047Z","updated_at":"2026-05-30T14:06:48.786007Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/23d134c7-f2bf-4c83-87f8-3938851bc707"},{"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":"df157081-449b-4687-984b-55a9b56309e6","company_id":"0d5cf132-03f2-47aa-8161-dcb1b95aca68","title":"Runtime Engineer","slug":"runtime-engineer-b8e275b2","description":"What MatX is Building \n MatX is building custom silicon for large-language-model inference and training, with HW/SW co-design across ISA, RTL, simulator, compiler, and kernels so each layer benefits from the others. The runtime owns the host-side stack and the contracts that bind those teams together.\n What You'll Do Here \n \n Build the host-side interface library — device memory management, DMA, streams and events, sync primitives — that every compiler-emitted program runs on top of\n Own and extend the executable format: the compiler→runtime contract, its versioning, the weight and quantization layouts that let compiler and runtime evolve independently\n Design the custom-kernel ABI — calling convention, sync semantics, lifecycle — and the host-side marshaling layer (DLPack, the buffer protocol, numpy) that gets Python tensors to the device\n Build Python bindings via PyO3, with a C-ABI shim as the alternative integration path for downstream consumers\n Build the LLM inference serving stack — paged KV cache, continuous batching, request scheduling, token streaming — and the cluster orchestration primitives underneath it\n Bring up interconnect topology from the host and own the failure-detection and clean-teardown path for stop-restructure-resume recovery across racks\n Design what the chip exposes to host-side profilers and debuggers — perf counters, traces, and the Python surfaces ML engineers actually use — and hit measurable performance targets on runtime overhead and serving throughput\n \n Who You Are \n \n Strong experience in a systems programming language — Rust, C, C++, or Go — including memory management, allocator design, and FFI/ABI work\n Have built Python interop layers in production (PyO3, ctypes, pybind11, or equivalent C-ABI bridging)\n Have designed and maintained API or ABI contracts between teams — versioning, evolution, breaking-change discipline — not just consumed someone else's\n Hands-on with at least one accelerator programming model (CUDA, ROCm, oneAPI Level Zero, TPU, or comparable) — enough to reason about device memory, async execution, and kernel launch\n ML-systems literate — comfortable with the training and inference loop, what collectives do, what a tensor layout is. Research depth not required.\n \n Bonus Points If You Have \n \n LLM inference internals — vLLM, TensorRT-LLM, or SGLang (paged attention, scheduler design)\n Rust at depth, including proc macros, unsafe with soundness reasoning, and complex lifetime/trait work\n Custom allocator design (slab, paged, arena) or other low-level memory work\n ML framework integration experience (PyTorch custom backends, JAX/XLA, ONNX runtime)\n Profiler or tracing infrastructure work (perfetto, Nsight, or a custom stack)\n Driver-adjacent or kernel-bypass work, or prior new-silicon bring-up\n \n Compensation \n The US base salary for this full-time position is determined based on a variety of factors including role, experience, location, job related skills, and relevant education and training. Career length is only a guideline for compensation.\n \n Early Career - $120,000 - $250,000 + equity\n Mid Career - $175,000 - $362,500 + equity\n Senior Career - $250,000 - $475,000 + equity\n \n What We Offer \n \n A Stake in our success  Generous equity, with option cash/equity swap at offer, and option to employee early exercise.\n Health \u0026 Wellness  Company subsidized Health, Dental, Vision, and Life insurance; Pre-tax Health Savings Accounts with generous company contribution (even if you don’t)\n Time To Recharge  4 weeks paid time off (accrued), 12 company holidays, and 3 weeks remote/flexible work per year\n Support to Parents  Up to 12 weeks of paid parental leave, regardless of your path to parenthood\n Learning \u0026 Development  $1,500 yearly towards your professional development e.g. conferences, courses, and other learning opportunities\n Team Connection  Team Lunches, quarterly off-sites, and regular town halls\n Financial Wellbeing.  401K and/or Roth IRA, with 5% company contribution, even if you don’t!\n Flexible Spending Accounts  Pre-tax spend accounts for medical, dental/vision, dependent care, parking, and transit expenses\n Commute On Us  For those commuting up to 1 hour, put your rideshare cost on our company card and reclaim the drive-time to get work done!\n MatX E[x]tras  $50 per month to use on the perks you care about most \n Remote Perks  We work remotely Monday \u0026 Friday, supported by home-tech setup, and remote wifi expense reimbursement\n \n As part of our dedication to the diversity of our team and our focus on creating an inviting and inclusive work experience, MatX is committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin or ancestry, sex, gender, gender identity, gender expression, sexual orientation, age, physical or mental disability, medical condition, marital/domestic partner status,","salary_min":250000,"salary_max":475000,"location":"Mountain View, CA","workplace":"remote","job_type":"full-time","experience_level":"principal","tags":["gpu","pytorch","cloud","llm"],"apply_url":"https://job-boards.greenhouse.io/matx/jobs/5231974008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T03:06:08Z","expires_at":"2026-06-29T14:13:28.359183Z","created_at":"2026-05-27T14:14:01.932882Z","updated_at":"2026-05-30T14:13:28.471451Z","company_name":"MatX","company_slug":"matx","company_logo_url":"https://www.google.com/s2/favicons?domain=matx.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/df157081-449b-4687-984b-55a9b56309e6"},{"id":"77cc20d9-b485-408a-9a85-753c8c333d3c","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior Software Engineer, App","slug":"senior-software-engineer-app-e60d648a","description":"Your Impact at LILA Scientists shouldn't have to context-switch between a dozen tools to go from hypothesis to result. We're building the platform that makes this a reality — and we need engineers who want to solve problems no one has solved before.\n We're hiring Sr Principal / Principal Software Engineers to design the agents, interfaces, and platform integrations that let researchers seamlessly collaborate with AI.\n About The Team \n The Application Team sits at the center of LILA — the integration point where Machine Learning, Life Sciences, Physical Sciences, and Software become one AI-native experience that carries a scientist from hypothesis to experiment to breakthrough results.\n \n AI isn't a feature here — it's the architecture. Agent frameworks, tools, and LLM orchestration are core primitives, not bolt-ons.\n The problems are genuinely hard. Connecting AI to automated lab workflows, ML pipelines, and multi-domain knowledge graphs means inventing patterns, not copying them.\n You'll learn domains you never expected. Working shoulder-to-shoulder with lab scientists and ML engineers means your technical surface area grows fast.\n You'll ship things that matter. The tools you build accelerate research timelines from months to days.\n \n If you want to build at the intersection of AI and science, move fast without breaking trust, and grow into the kind of engineer who can architect systems that don't exist yet — we want to talk.\n \n What You'll Be Building \n \n Design \u0026 Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.\n Database Architecture \u0026 Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.\n Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.\n Performance \u0026 Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.\n Cloud \u0026 Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.\n Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.\n \n What You'll Need To Succeed \n \n Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.\n 5-8+ years of engineering experience building and deploying large-scale systems in production. You must be strong in backend.\n Full Stack Development : Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)\n Hands on experience using AI coding assistants to drive productivity is required.\n Communication \u0026 Collaboration : Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.\n Problem Solving : Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.\n \n Bonus Points For \n \n Applied AI Engineering: Experience building with AI agents, graph-based workflows, tool-use protocols (MCP), RAG pipelines, or LLM orchestration frameworks.\n Cloud \u0026 DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).\n Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django).\n Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).\n Familiarity with Python for Science : Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).\n Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.\n Experience with laboratory devices, robotics, or hardware drivers.\n \n \n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to","salary_min":144000,"salary_max":240000,"location":"Boston, MA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["agents","pytorch","robotics","data-pipeline","llm","embeddings","cloud","rag"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4248042009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-26T16:55:46Z","expires_at":"2026-06-29T14:17:43.519875Z","created_at":"2026-05-27T14:18:34.581118Z","updated_at":"2026-05-30T14:17:43.627321Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/77cc20d9-b485-408a-9a85-753c8c333d3c"},{"id":"83ca8ffa-09ac-4942-a639-4e6c4b482642","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior Software Engineer, Operations Research","slug":"senior-software-engineer-operations-research-e517b660","description":"Your Impact at LILA \n We are a cross-functional team (Software and Robotics) developing orchestration algorithms (instrument scheduling and robot routing) and lab simulation capabilities. We are building the muscles of the lab, which translate the AI brain's ideas into efficient robotic movements. Our work involves building data pipelines to feed the orchestration algorithms. We work with robotics scientists to build and deploy the algorithms on our software platform and ensure they meet scientific constraints.\n We are seeking a Senior Software Engineer, Operations Research to join our software group and help build the next generation AI-driven scientific platform. In this role, you will design, build, and optimize backend systems and data infrastructure that power orchestration and lab execution. You will focus on developing services, high-performance APIs, databases, and ensuring the reliability of systems that integrate advanced AI frameworks with complex scientific workflows.\n You'll work closely with robotics researchers, platform engineers, and scientists to develop systems that can handle diverse workloads and scale to demanding throughput. This is an opportunity to apply your engineering expertise to a cutting-edge AI platform with real scientific impact. If you are passionate about building performant and elegant systems, we would love to hear from you.\n What You'll Be Building \n \n (Fleet) orchestrator, Scheduler, Manufacturing Execution System, data pipelines, and related software systems.\n Design \u0026 Build APIs: Design and build APIs and backend services that integrate with AI-driven applications, with focus on reliability and performance.\n Database Architecture \u0026 Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.\n Application Development: Drive the implementation of backend services, focusing on performance, maintainability, and reliability.\n Performance \u0026 Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.\n Cloud \u0026 Infrastructure: Build and deploy production-grade systems on AWS using Kubernetes and modern DevOps practices.\n Cross-Functional Collaboration: Work with robotics scientists, platform engineers, and ML teams to integrate data pipelines and orchestration into scientific workflows.\n \n What You'll Need to Succeed \n \n Bachelor's or Master's degree in Computer Science, Engineering, or related field.\n 5–10 years of engineering experience building and deploying large-scale backend or data systems in production.\n Backend / Data Development: Experience developing distributed software and data systems (Postgres, Flyte, Temporal, NATS/MQTT, FastAPI).\n Hands-on experience using AI coding assistants to drive productivity is required.\n Communication \u0026 Collaboration: Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.\n Problem Solving: Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.\n \n Bonus Points For \n \n Experience developing scheduling software or manufacturing execution systems.\n Experience with operations research solvers (OR-Tools, HiGHS, Gurobi).\n Cloud \u0026 DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).\n Familiarity with Python for Science: Familiarity with data science, data visualization, and ML libraries (pandas, polars, numpy, scipy, pytorch).\n Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $180,000 — $256,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA bui","salary_min":180000,"salary_max":256000,"location":"Boston, MA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["pytorch","robotics","cloud","data-pipeline","embeddings","research"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4246973009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-26T15:22:46Z","expires_at":"2026-06-29T14:17:43.904427Z","created_at":"2026-05-27T14:18:35.008145Z","updated_at":"2026-05-30T14:17:44.01629Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/83ca8ffa-09ac-4942-a639-4e6c4b482642"},{"id":"25353da4-ae66-4acf-b4b6-fea4e00fda29","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior Software Engineer, Data","slug":"senior-software-engineer-data-e29e2009","description":"Your Impact at LILA \n Join us in shaping the future of science! We are seeking Senior Software Engineers with backend experience to join our Data Platform Team (Data), where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!\n About The Team \n The Data Platform Team (Data) builds and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.\n What You'll Be Building \n \n Design \u0026 Build APIs: Design and build high-performance, secure, and well-documented APIs that integrate with AI-driven applications.\n Database Architecture \u0026 Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.\n Performance \u0026 Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.\n Cloud \u0026 Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.\n Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.\n \n What You'll Need To Succeed \n \n Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.\n 5-8+ years of engineering experience building and deploying large-scale backend systems in production.\n Cloud \u0026 DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).\n Experience with ORMs: Experience with and web services for CRUD services (SQL Alchemy, SQLModel, FastAPI, Django).\n Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).\n Full Stack Development : Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)\n Hands on experience using AI coding assistants to drive productivity is required.\n Communication \u0026 Collaboration : Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.\n Problem Solving : Proven ability to deliver backend solutions, balancing trade-offs between scalability, performance, and maintainability.\n \n Bonus Points For \n \n Familiarity with Python for Science : Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).\n Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.\n Experience with laboratory devices, robotics, or hardware\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $144,000 — $288,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values ","salary_min":144000,"salary_max":288000,"location":"Boston, MA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["robotics","data-pipeline","pytorch","cloud","embeddings"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4250077009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-26T15:22:23Z","expires_at":"2026-06-29T14:17:43.668505Z","created_at":"2026-05-27T14:18:34.759394Z","updated_at":"2026-05-30T14:17:43.783774Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/25353da4-ae66-4acf-b4b6-fea4e00fda29"},{"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"}],"page":1,"per_page":20,"total":1030,"total_pages":52}