Machine Learning Infrastructure Engineer - Supercomputing

At Physical Intelligence, we are pioneering general-purpose AI applications for the physical world. Our innovative approach involves orchestrating thousands of accelerators across a diverse ecosystem of GPU and TPU clusters, which encompass various hardware generations, cloud platforms, and cluster configurations.Researchers frequently encounter challenges i…

Role overview Whatnot seeks a Software Engineer specializing in Machine Learning Infrastructure to develop and maintain the systems powering its machine learning applications. This position is based in San Francisco, CA and centers on building the technical backbone that supports machine learning efforts across the company. What you will do Develop and improve frameworks that enable machine learning throughout Whatnot’s platforms. Collaborate with teams from multiple disciplines to design infrastructure that can scale as needs grow. Support seamless integration of machine learning models into existing products.

About Hive Hive stands at the forefront of cloud-based AI solutions, revolutionizing the way organizations understand, search, and generate content. Trusted by some of the largest and most innovative companies globally, Hive empowers developers with a robust collection of pre-trained AI models that handle billions of API requests each month. Our turnkey software applications utilize proprietary AI models and datasets, enabling transformative use cases in various sectors, including content moderation, brand protection, and context-based ad targeting. As we continue to grow with over $120M in funding from esteemed investors such as General Catalyst and Bain & Company, we are excited to expand our global team of over 250 talented professionals across our San Francisco, Seattle, and Delhi offices. Join us in pioneering the future of AI!Position Overview: Senior Machine Learning EngineerTo propel our ambitious vision forward, we are seeking a dynamic Senior Machine Learning Engineer to join our elite team. This role is perfect for individuals eager to lead the charge in deep learning technology, where you will design, prototype, and deploy cutting-edge neural network models into production. We value candidates who thrive on solving complex challenges with terabyte-scale datasets, are enthusiastic about mastering new technologies across the machine learning spectrum, and take initiative in driving their projects to completion. The ideal candidate will possess a proven track record of developing a machine learning project from inception to deployment, contribute groundbreaking ideas and innovative implementations, and demonstrate the ability to architect scalable, maintainable data pipelines that optimize our AI solutions.

Orchard Robotics is an innovative Series A startup, supported by leading venture capital firms including Quiet Capital, Shine Capital, and General Catalyst. Our mission is to enhance America’s agricultural efficiency by creating the AI farmer that revolutionizes farming practices. With over $25 million raised, we are dedicated to helping farmers operate with greater profitability and sustainability.Our approach begins with collecting vital data for farmers, providing insights into the growth of their millions of trees across expansive farmland. Using sophisticated camera systems designed in-house, we capture images of billions of fruits on farms. This data is integrated into our proprietary cloud platform, FruitScope, enabling farmers to manage their crops with unparalleled precision.Farmers nationwide rely on our cutting-edge software to analyze their data, make informed decisions, and oversee daily farming operations. Our technology is currently deployed on some of the largest farms in the country.The Role:To analyze the vast array of fruits on farms year-round, our advanced tractor-mounted camera systems must accurately identify their location and the characteristics of the fruits they capture. We are seeking a Senior Machine Learning Engineer to develop innovative, effective, and durable solutions for machine learning and computer vision problems, focusing on training edge machine learning models using extensive real-world image data collected from our camera systems.About the Position:As a foundational member of our engineering team, you’ll enjoy substantial equity compensation.This is a full-time role based in our San Francisco, CA office.We offer flexible work hours to accommodate your schedule.Comprehensive health, vision, and dental insurance is provided, with 100% of the premium covered.Our fast-paced environment may occasionally require extended hours or weekend work.You will collaborate closely with our CEO and a dedicated team in a close-knit atmosphere.We are motivated by the significant impact of our work—every line of code and system we develop reduces food waste and ensures more people are fed.

At Exa, we are revolutionizing the way AI applications access information by building a cutting-edge search engine from the ground up. Our team is dedicated to developing a robust infrastructure capable of crawling the web, training advanced embedding models, and creating high-performance vector databases using Rust to facilitate seamless searches.As part of our ML team, you'll be instrumental in training foundational models that refine search capabilities. Our mission? To deliver precise answers to even the most complex queries, effectively transforming the web into an incredibly powerful knowledge database.We are seeking a talented Machine Learning Research Engineer who is passionate about crafting embedding models that enhance web search efficiency. Your responsibilities will include innovating novel transformer-based architectures, curating extensive datasets, conducting evaluations, and continuously improving our state-of-the-art models.

Join us at Foxglove, where we are revolutionizing the robotics industry by building robust data infrastructure for real-world applications.As robotics transitions from research environments to practical implementations in factories, warehouses, vehicles, and field operations, data becomes essential for engineers to troubleshoot failures, understand unexpected behaviors, and enhance robotic systems.At Foxglove, we provide the observability, visualization, and data infrastructure that enable robotics and autonomous systems teams to efficiently ingest, store, query, replay, and analyze extensive volumes of multimodal sensor data from live systems and production fleets.About the RoleWe are seeking a talented Applied Machine Learning Engineer with strong infrastructure insights to design, deploy, and scale the machine learning systems that power our data platform. In this impactful role, you will be responsible for optimizing production ML infrastructure—from enhancing inference pipeline throughput to establishing training and evaluation workflows. You will focus on high-priority challenges, such as developing retrieval applications for petabyte-scale multimodal robotics data, utilizing cutting-edge models to create high-performance search and data mining products, and fostering an internal ML flywheel for rapid iteration. This is a hands-on, application-driven position rather than a research-focused role.Key ResponsibilitiesDeploy and manage inference infrastructure for production ML workloads, focusing on model serving, scalability, and cost efficiency.Build and oversee vector database integrations and embedding applications to facilitate semantic search across various multimodal robotics data types (image, video, point cloud, and time series).Design and implement evaluation and training infrastructure to enhance model performance rapidly.Lead cloud architecture decisions and tools to optimize inference latency, throughput, cost, and reliability at scale.Collaborate closely with product engineers to deliver application-driven ML features that empower developers at the forefront of robotics and physical AI, steering clear of prototype experiments.Identify appropriate off-the-shelf solutions for production and determine when to build versus buy.

Be a Part of the Revolution in E-Commerce with Whatnot!Whatnot stands as the leading live shopping platform across North America and Europe, where you can buy, sell, and explore the items you cherish. We are transforming the landscape of e-commerce by merging community engagement, shopping, and entertainment into a unique experience tailored just for you. As a remote-first team, we are driven by innovation and firmly rooted in our core values. With operational hubs in the US, UK, Germany, Ireland, and Poland, we are collaboratively crafting the future of online marketplaces.From fashion and beauty to electronics and collectibles like trading cards, comic books, and live plants, our live auctions cater to a diverse audience.And this is just the beginning! As one of the fastest-growing marketplaces, we are on the lookout for innovative, forward-thinking problem solvers in all areas of our business. Stay updated with the latest from Whatnot through our news and engineering blogs, and join us in empowering individuals to transform their passions into successful ventures while fostering community through commerce. The RoleWe are seeking passionate builders—intellectually curious, entrepreneurial engineers who are ready to pioneer the future of AI and ML at Whatnot. You will be responsible for designing and scaling the foundational infrastructure that supports machine learning and self-hosted large language model applications throughout the organization. Collaborating closely with machine learning scientists, you will facilitate the deployment of cutting-edge models into production, creating entirely new product experiences. Your work will involve constructing systems that ensure advanced machine learning is reliable and efficient at scale—from low-latency model serving to distributed training and high-throughput GPU inference.Your Responsibilities:Lead the infrastructure that powers AI and ML models across vital business domains—enhancing growth, trust and safety, fraud detection, seller tools, and more.Prototype, deploy, and operationalize innovative ML architectures that significantly influence user experience and marketplace dynamics.Design and scale inference infrastructure capable of managing large models with minimal latency and maximal throughput.Construct distributed training and inference pipelines utilizing GPUs, as well as model and data parallelism.Push the boundaries of your expertise and explore new technologies and methodologies.

Join Reducto as a Machine Learning EngineerAt Reducto, we empower AI teams to harness real-world enterprise data with unparalleled precision.Much of the enterprise data—ranging from financial documents to healthcare records—remains trapped in unstructured formats such as PDFs and spreadsheets. Our vision models are designed to interpret these documents in a human-like manner, enabling the development of innovative products, training of machine learning models, and automation of processes on a large scale.Our rapid growth is a testament to our success, having achieved a staggering 7x year-over-year revenue increase, collaborating with numerous companies from prominent AI teams like Harvey, Vanta, and Scale to major enterprises including FAANG and leading trading firms.With over $100 million raised from esteemed investors such as A16z, Benchmark, and First Round Capital, we are on the lookout for a talented Machine Learning Engineer to assist in training and deploying models crucial for our core product's success.

Join Arcade as a Senior Machine Learning Engineer, where you'll be at the forefront of AI innovation. In this pivotal role, you will leverage advanced machine learning algorithms to create and enhance cutting-edge solutions. Collaborate closely with cross-functional teams to drive impactful projects from ideation to deployment.

Airbnb’s Core Machine Learning team in Community Support builds intelligent solutions for customer service, using technologies like Agentic AI. This remote role focuses on developing and integrating advanced AI tools that enhance the support experience for millions of users. Role overview As a Machine Learning Engineer for Customer Support Engineering, the work centers on designing and launching new ML systems and product integrations. Projects include building Chat AI assistants, Voice AI assistants, and other tools that help support agents and customers. The team works with methods such as Supervised Fine-Tuning (SFT), Reinforcement Learning, Distillation, Retrieval-Augmented Generation (RAG)/Search, and automating LLM testing. What you will do Lead the development of new machine learning systems and strategies to address real-world customer support challenges. Collaborate with product managers, designers, and engineers to improve Airbnb’s customer service products. Impact This position offers the chance to apply advanced AI to make customer service more personalized, intuitive, and proactive. Many initiatives start from concept, so there is room to shape ideas into practical solutions that influence the future of Airbnb support for both guests and hosts.

Are you ready to push the boundaries of what you believe you're capable of? At Coinbase, our vision is to enhance economic freedom globally. This is a grand, ambitious endeavor that challenges us to deliver our best every day as we construct the foundational onchain platform and shape the future of the global financial system.To drive our mission forward, we are in search of a unique candidate. We seek an individual who is not only passionate about our objective but also believes in the transformative power of cryptocurrency and blockchain technology to revolutionize the financial landscape. We are looking for someone eager to make a significant impact, who thrives under pressure while collaborating with a team of highly skilled professionals, and who actively seeks constructive feedback for continuous improvement. We want a problem-solver who embraces challenges head-on.Our work culture is intense and not suited for everyone. However, if you aspire to build the future alongside exceptional individuals and are ready to meet high expectations, this is the place for you.While many positions at Coinbase are remote-first, we are not solely remote. In-person engagements are expected throughout the year. We conduct team and company-wide offsites several times a year to promote collaboration, connection, and alignment. Your attendance is both expected and fully supported.We are looking for a Senior Machine Learning Platform Engineer to join our Machine Learning Platform team. This team is responsible for developing the core components for feature engineering, as well as training and serving ML models at Coinbase. Our platform plays a crucial role in combating fraud, personalizing user experiences, and analyzing blockchains. You will have the opportunity to leverage your engineering expertise across various aspects of large-scale ML development, including stream processing, distributed training, and highly available online services.

Waymo is at the forefront of autonomous driving technology, driven by the mission to become the world’s most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has dedicated itself to developing the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access while preventing traffic-related fatalities. Our Waymo Driver is the backbone of our fully autonomous ride-hail service and is adaptable across a variety of vehicle platforms and applications. To date, we have successfully completed over ten million rider-only trips, supported by our extensive experience of driving autonomously for more than 100 million miles on public roads and tens of billions of miles in simulation across over 15 U.S. states.The Driver Understanding and Evaluation team at Waymo focuses on deeply understanding the Waymo Driver’s behavior. With an impressive rate of over 1 million driverless miles per week, it is essential for Waymo to analyze and evaluate the behavior of its vehicles—both in real-world scenarios and simulations—using automated algorithms. Our learned metrics team plays a crucial role in leveraging machine learning to scale our operations to meet Waymo's ambitious goals. We work collaboratively across teams to integrate machine learning into production systems and establish the reward function for Waymo. Our team designs and manages large-scale machine learning systems, data infrastructures, simulation workflows, and analytical tools. By combining expert human insights with advanced machine learning models, we provide critical training and evaluation data for the Waymo Driver. We are on the lookout for passionate researchers and software engineers dedicated to creating robust machine learning systems for our autonomous vehicles, with a relentless pursuit of enhancing the performance of our technology stack.

About SunoSuno is revolutionizing the music industry by harnessing the power of advanced AI technology to inspire creativity. Our innovative platform, which includes the groundbreaking Suno Studio, provides an exceptional generative audio workstation designed for everyone—from casual singers to aspiring songwriters and seasoned musicians. Suno is dedicated to empowering a diverse global community to create, share, and explore music, celebrating the joy of musical expression for all.About the RoleWe are seeking a visionary leader to spearhead our recommendations team at Suno. In this pivotal role, you will be at the forefront of developing our music discovery and recommendation systems, shaping how millions of users engage with music on our platform. Your expertise will drive the evolution of our systems while fostering a collaborative and innovative team environment.This position is ideal for an individual with extensive experience in scaling recommendation systems and a passion for crafting a superior user experience. If you are excited to apply your skills in a dynamic setting and lead a talented team, we want to hear from you!Discover more about this role at Suno!What You'll DoDefine and execute Suno's vision and strategy for recommendations, setting the technical direction for the team.Collaborate with leaders across product, engineering, and research to ensure our recommendations evolve in alignment with platform growth.Lead the design and development of a comprehensive recommendation system, from initial prototyping to large-scale deployment.Recruit, mentor, and expand a high-performing recommendations team.What You'll NeedA minimum of 5 years of experience in building large-scale recommendation systems, with at least 2 years in a leadership role overseeing development.Profound technical knowledge of cutting-edge technologies and methodologies in recommendation systems, along with a pragmatic approach to implementation.Exceptional collaborative skills with a proven ability to influence cross-functional teams.A genuine passion for Suno's mission and a keen interest in shaping the future of music discovery.Bachelor’s degree or equivalent experience.Additional Notes: Candidates must be eligible to work in the United States.This role requires onsite presence in San Francisco.

Innovate Boldly. Shape Tomorrow. Our VisionCrafting everyday AGI. Reliable, consumer-friendly agents that transform human-AI synergy for millions. Our software is designed to act as a collaborator, enhancing your daily capabilities.Why Choose AGI, Inc.?We are a discreet collective of exceptional founders and AI pioneers, whose expertise spans Stanford, OpenAI, and DeepMind. Our team leads the way in mobile and computer-based agents, scaling these innovations for consumer use.With a foundation rooted in extensive research on agents, our AI prioritizes trustworthiness and reliability as fundamental principles.Backed by top-tier investors who previously supported the first wave of AI leaders, we are now positioned to create the next generation: everyday AGI. (Check out the demo)If you envision possibilities where others perceive restrictions, continue reading.Your RoleTraining Automation: Design and execute robust CI/CD pipelines tailored for machine learning workflows. Automate nightly and on-demand training sessions encompassing data ingestion, job orchestration, checkpointing, and artifact management, with a focus on reliability.Evaluation Infrastructure: Develop scalable evaluation frameworks that automatically benchmark models with each merge. Enhance latency and resource efficiency to ensure quick experimentation and immediate detection of performance regressions.Research Tooling: Create internal SDKs, CLIs, and lightweight UIs (e.g., Streamlit, Retool) empowering researchers to:Examine trajectories and tracesVisualize model failuresOrganize and oversee datasetsIterate seamlesslyYou'll facilitate a user-friendly experimentation process.Observability & Performance: Enforce comprehensive tracking for:Model latency, throughput, and error ratesGPU utilization, and more.

About the Role:The Machine Learning team at Tubi is at the forefront of transforming user experiences through cutting-edge technology. With the industry's largest inventory and a vast audience of millions, we are dedicated to solving complex challenges in recommendations, search, content understanding, and ad optimization, shaping the future of streaming.We are on the lookout for a Director of Machine Learning Engineering and Infrastructure to spearhead a hybrid team that merges advanced ML engineering with exceptional infrastructure design. In this pivotal role, you will define the strategic vision and implementation for scaling our machine learning capabilities, ensuring our distributed systems and infrastructure can foster innovation on a grand scale. You will blend technical expertise with outstanding leadership to guide teams in delivering robust ML systems and high-performance distributed services.

Join Pinterest as a Manager II in Machine Learning Engineering and lead a team of talented engineers and data scientists in developing innovative machine learning solutions. You will play a crucial role in driving the technical direction of our projects, ensuring the delivery of high-quality models and algorithms that enhance user experience and engagement. You will collaborate with cross-functional teams and stakeholders to understand business needs and translate them into technical requirements.

About Quizlet:At Quizlet, our vision is to empower every learner to achieve their educational goals in the most effective and enjoyable manner. As a thriving $1B+ educational platform, we serve two-thirds of U.S. high school students and half of college students, facilitating over 1 billion learning interactions weekly.By integrating cognitive science with advanced machine learning techniques, we tailor and enhance the learning experience for students, professionals, and lifelong learners alike. Our enthusiasm lies in the potential to support more learners through diverse methodologies and tools.Let's Shape the Future of Learning TogetherJoin us in designing and implementing AI-driven learning solutions that scale globally, unlocking the potential of learners everywhere.About the Team:The Personalization & Recommendations team is dedicated to crafting customized learning experiences that enable millions of learners to study more effectively. We are seeking Machine Learning Engineers across Senior to Staff levels (including Sr. Staff) to join our innovative team.You will leverage your expertise in modern recommender systems—encompassing deep learning-based retrieval, embeddings, and multi-stage ranking—to enhance Quizlet's personalization capabilities. Collaborating at the nexus of machine learning, product development, and scalable systems, you will ensure our recommendations are efficient, ethical, and aligned with learner outcomes, privacy, and fairness.This is an onsite position, requiring team members to work in the office at least three days a week: Monday, Wednesday, and Thursday, as well as additional days as needed. We believe this in-office collaboration fosters efficiency, enhances teamwork, and promotes both personal and organizational growth.

Role overview OpenAI seeks a Machine Learning Engineer to focus on API development in multicloud settings. This role is based in San Francisco and centers on advancing the capabilities of API products across different cloud providers. What you will do Use advanced machine learning techniques to enhance API offerings Collaborate with colleagues from various specialties to launch new features and refine existing ones Support ongoing improvements and innovation for API services that operate across multiple cloud environments

About TroveTrove is pioneering an advanced AI assistant tailored for financial institutions, encompassing enterprise search and intelligent agents for private equity firms, hedge funds, and banks.Our mission is to bring associate-level artificial general intelligence (AGI) to the forefront.We have successfully raised nearly $10 million in funding from renowned investors, including Menlo Ventures and Khosla Ventures.Our initial traction is impressive, featuring significant enterprise contracts and user engagement levels that rival top-tier B2B AI applications.Why Join Us?Impactful Role: From your first day, you'll play a critical role in shaping our product and culture, tackling unexplored challenges in generative AI applications, including enterprise-scale data retrieval, autonomous agents, and the development of knowledge graphs.Competitive Compensation: Enjoy a compensation package that includes over 0.5% equity and a salary range between $175,000 and $300,000, allowing you to hold a significant stake in the company.Talented Team: Join a remarkable founding team in San Francisco, featuring leaders with proven track records, such as Shivaal Roy (CTO), a former founding engineer at Glean with over $100 million ARR, and Danny Goldman (CEO), a high achiever in Bain's Private Equity group.Your ResponsibilitiesAdvance the frontiers of generative AI by implementing human-level capabilities.Create and implement innovative algorithms for knowledge graphs to enhance search and retrieval processes across both structured and unstructured firm data.Collaborate with leading AI laboratories and top-tier financial institutions to develop reliable, AI-driven systems.About YouDemonstrated history of outstanding performance in software engineering.3 to 8 years of relevant experience in software engineering.Eager to work in-person four days a week in San Francisco.Preferred but not mandatory: familiarity with large language models, search and retrieval systems, and previous experience in the financial sector.

About NomadicMLAt NomadicML, we are harnessing the power of artificial intelligence to revolutionize the way machines understand and interpret motion. Our vision-language models (VLMs) transform vast amounts of video data into actionable insights, paving the way for advancements in self-driving technology, robotics, and industrial automation.Founded by Mustafa Bal and Varun Krishnan, both alumni of Harvard University, our team is comprised of experts who have previously developed critical AI systems at industry giants like Snowflake, Lyft, Microsoft, Amazon, and IBM Research. With a commitment to innovation, we are dedicated to mining insights from the 5 trillion miles driven by Americans annually, uncovering the next frontier in machine intelligence.About the RoleWe are looking for a passionate Machine Learning Engineer who excels at the intersection of foundational model research and production engineering. In this role, you will play a key part in optimizing how machines learn from motion, focusing on training and refining large-scale Vision-Language Models that analyze complex real-world video data.You will be responsible for creating multi-modal architectures that accurately perceive, localize, and describe motion events across millions of video frames, transforming these innovations into robust APIs and SDKs for enterprise clients.Working closely with the founders, your contributions will include:Training and assessing VLMs tailored for motion comprehension within autonomous driving and robotics datasets.Designing and scaling GPU-accelerated pipelines for training, fine-tuning, and inference on diverse data types (video, language, and sensor metadata).Developing evaluation frameworks that benchmark spatiotemporal reasoning and localization precision.