Lead Staff Machine Learning Platform Engineer

About FaireFaire is a transformative online wholesale marketplace, driven by the conviction that local businesses are the future. Independent retailers around the globe generate more revenue than massive corporations like Walmart and Amazon combined, yet individually, they remain small. At Faire, we harness technology, data, and machine learning to connect this vibrant community of entrepreneurs. Think of your favorite local boutique — we empower them to discover and sell the best products from around the world. With our innovative tools and insights, we aim to level the playing field, enabling small businesses to thrive against larger competitors.By championing the growth of independent businesses, Faire positively impacts local economies on a global scale. We’re in search of intelligent, resourceful, and passionate individuals to join us in fueling the shop local movement. If you value community, we invite you to be part of ours.About this RoleAs the Senior Staff Machine Learning Platform Engineer, you will spearhead the technical vision and evolution of Faire's ML platform. You will establish standards, influence organization-wide architecture, and lead intricate, cross-functional initiatives that enhance data science velocity at scale. This position is crucial for adapting ML workflows to leverage modern AI productivity tools. You will not only develop models but also design the systems that enable those models to empower tens of thousands of small retailers in competing and growing their local businesses.

Join us in creating the backbone of data infrastructure for real-world robotic operations.As robotics transitions from research labs to real-world applications across factories, warehouses, vehicles, and field deployments, understanding the intricacies of robotic performance becomes critical. When robots encounter failures or unexpected behaviors, data analysis is key to deciphering the underlying issues.At Foxglove, we are at the forefront of building tools for observability, visualization, and data infrastructure that empower robotics and autonomous systems teams to manage, analyze, and derive insights from vast amounts of multimodal sensor data collected from operational systems and production fleets.Role OverviewWe are seeking a passionate ML Platform Engineer with robust infrastructure expertise to design, deploy, and scale our data platform systems. This platform-centric role will allow you to take charge of the infrastructure layer that facilitates machine learning in production environments, going beyond just the models themselves.Your responsibilities will encompass ensuring the reliability, scalability, and performance of the ML platform, including areas such as inference serving, pipeline orchestration, training infrastructure, and evaluation frameworks. You will be tackling substantial challenges such as managing petabyte-scale multimodal robotics data and optimizing high-throughput retrieval and embedding pipelines in a hands-on infrastructure capacity.Key ResponsibilitiesDesign and operationalize production inference infrastructure, focusing on model serving, autoscaling, load balancing, and cost efficiency across cloud environments.Own the platform architecture for embedding and retrieval pipelines that enable semantic search across multimodal robotics data (image, video, point cloud, and time series).Develop and sustain the training and evaluation infrastructure that supports rapid model performance iteration, including job orchestration, experiment tracking, and dataset versioning.Lead decisions on cloud infrastructure (AWS/GCP) that affect latency, throughput, reliability, and scalability.Establish platform abstractions and internal tools that empower product engineers to deliver ML-enhanced features without managing infrastructure directly.Assess, integrate, and operationalize third-party ML infrastructure components while establishing clear build vs. buy frameworks for the team.

tvScientific, powered by Pinterest, develops a connected TV (CTV) advertising platform designed for performance marketers. The platform combines media buying, optimization, measurement, and attribution to automate and improve TV advertising. Built by professionals in programmatic advertising, digital media, and ad verification, tvScientific aims to deliver measurable results for advertisers. Role overview As a Machine Learning Platform Engineer, you will join a team that operates where Site Reliability Engineering meets low-latency distributed systems. This team advances Pinterest’s real-time machine learning and measurement infrastructure, focusing on sub-millisecond decision-making and high-throughput data access. Seamless integration with Pinterest’s core stack is central to the work. What you will do Design and build systems to keep queries and RPCs fast and reliable, even during periods of heavy demand. Develop and enhance the foundation of the machine learning training and serving stack. Address challenges in storage, indexing, streaming, fan-out, and managing backpressure and failures across services and regions. Collaborate with software engineering, data infrastructure, and SRE teams to ensure systems are observable, debuggable, and ready for production. Key areas of focus I/O scheduling and batching Lock-free or low-contention data structures Connection pooling and query planning Kernel and network tuning On-disk layout and indexing strategies Circuit-breaking and autoscaling Incident response and failure management NixOS Defining and maintaining SLIs and SLOs This position is a strong fit for engineers interested in building and operating large-scale infrastructure, particularly those who enjoy working on real-time systems, observability, and reliability.

About Us:At Ambience Healthcare, we are not just another scribe; we are pioneering an AI intelligence platform that reinvigorates the human touch in healthcare while delivering significant ROI for health systems nationwide.Our innovative technology enables healthcare providers to concentrate on delivering exceptional care by alleviating the administrative burdens that detract from patient interactions and their most impactful work. Ambience provides real-time, coding-aware documentation and clinical workflow support in ambulatory, emergency, and inpatient settings across leading health systems in North America.Our team is driven by a relentless pursuit of excellence and extreme ownership, dedicated to crafting the best solutions for our health system partners. We champion transparency, positivity, and thoughtful engagement, holding each other accountable because we understand the significance of the challenges we tackle.Ambience has earned accolades such as being ranked #1 for Improving the Clinician Experience in the KLAS Research Emerging Solutions Top 20 Report, being recognized by Fast Company as one of the Next Big Things in Tech, and being named one of the best AI companies in healthcare by Inc. We were also selected as a LinkedIn Top Startup in 2024 and 2025. Our esteemed investors include Oak HC/FT, Andreessen Horowitz (a16z), OpenAI Startup Fund, and Kleiner Perkins — and our journey is just beginning.The Role:As a Staff Machine Learning Engineer, you will play a crucial role in advancing clinical AI that impacts millions of patient encounters across the largest health systems in the nation. Your contributions will directly influence the speed at which we enhance our AI capabilities through the platform you will oversee.You will design and implement evaluation and release processes that empower teams to deliver with confidence, create observability tools to identify quality issues pro-actively, and develop debugging tools that facilitate rapid issue reproduction. Additionally, you’ll work on the chart context retrieval layer that transforms patient history into model-ready inputs.Our goal is to enable teams to iterate on quality within days, not weeks, ensuring that every enhancement you implement adds value across all product teams each quarter.Please note that our engineering roles operate in a hybrid model from our San Francisco office (3 days per week).What You’ll Own:Evaluation & Release Infrastructure — Developing automated grading systems and release gates that function seamlessly across product teams, creating a unified evaluation dataset with version control to replace fragmented workflows. Implementing production-quality monitoring that includes end-to-end tracing, shared metrics, and automated alerts.Debugging Tools — Building encounter replay features that reconstruct precise inference inputs (including retrieved chart context, packed prompts, and model versions) to allow teams to troubleshoot issues without sifting through logs. Creating differential views to compare known good states with regressions.

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.

Airbnb began in 2007 with two hosts and three guests in San Francisco. Since then, the platform has grown to over 5 million hosts and more than 2 billion guests worldwide. Airbnb connects people with unique places to stay and experiences, building authentic community connections across nearly every country. The team: Growth Platform Engineering The Growth Platform team focuses on driving sustainable, long-term growth for Airbnb. The team’s mission centers on building an agentic system and supporting capabilities to help all Airbnb offerings grow, both now and in the future. Efforts include delivering personalized and relevant content and product experiences to users, both on and off the Airbnb platform. The team is working toward a future where AI identifies opportunities, creates campaigns, personalizes experiences, and optimizes outcomes with minimal human input. This journey moves through a maturity curve: AI-assisted, agentic, and ultimately autonomous systems, always with human oversight to ensure brand safety, quality, and compliance. Growth Platform Engineering is tightly integrated with the Airbnb product, enhancing the customer journey and enabling new ways for users to engage. The platform supports a range of digital marketing channels, landing pages, email, push notifications, SMS, and digital advertising, as well as the machine learning and data infrastructure that powers these efforts. What you will do Develop AI-driven solutions to shape the future of Airbnb’s agentic growth platform, using the latest AI methodologies. Lead and mentor engineers through brainstorming, design, and implementation of AI products and features, from initial concept to deployment. Work at the intersection of technical depth, architectural innovation, and mentorship as a Senior Staff Engineer. Collaborate with cross-functional teams to build scalable systems that operate globally. Help evolve the foundational elements of Airbnb’s AI-powered growth systems.

Key Responsibilities:Serve as a pivotal member of the leadership team, shaping strategy and vision for the organization.Establish the functional and research roadmap to drive business growth.Guide, mentor, and expand the data science team, fostering professional development.Utilize high-quality, diverse datasets from Fortune 500 companies to extract actionable insights for our clients.Design, prototype, and refine innovative architectures in Natural Language Processing (NLP), including Dual-Encoders, Retrieval-Based Ranking Engines, and Generative Models.Establish thought leadership in the industry by sharing insights through blogging and speaking engagements at conferences.

We are looking for a talented individual who is local to any of our offices (Silver Spring, NYC, SF, Miami, Denver) and is eager to work at least 1-2 times per week from one of these locations.ABOUT ROCKET MONEY At Rocket Money, our mission is to empower individuals to take control of their financial lives. We provide our members with unparalleled insights into their finances and a suite of services that save them both time and money, enabling them to achieve their financial goals.ABOUT THE TEAM As Machine Learning Engineers at Rocket Money, we play a vital role in enhancing customer engagement with our diverse range of financial products. Our responsibilities include transaction enrichment, personalization, and creating cross-functional tools that bolster various AI initiatives. Collaborating closely with product teams, we develop features that aid customers in understanding, tracking, and improving their personal finances. We value team players who excel in cross-team collaboration, can align strategy with ML and AI-driven user experiences, deliver scalable and high-quality user experiences, and are mindful of the impact our products have on end users. At the Staff level, you will be expected to cultivate broad expertise in our products and the ML solutions that enhance them, while driving technical advancements within the team.ABOUT THE ROLE As a Staff Machine Learning Engineer, you will spearhead our ML and AI product development efforts, utilizing your expertise to design, implement, and maintain sophisticated ML systems that elevate our product experiences. Your responsibilities will include:Leading the architecture and development of advanced AI and ML features across Rocket Money's product suite, proactively identifying and addressing technical challenges.Designing and maintaining robust evaluation frameworks to ensure continual improvement of ML/AI systems and facilitating similar initiatives among others.Creating innovative product experiences that leverage our unique dataset and scalability, guiding others in delivering impactful results through effective technical leadership and collaboration with product teams.Overseeing the end-to-end development and implementation of ML and AI product features in partnership with cross-functional product teams, emphasizing thorough technical critique and clear communication of business impacts.Providing technical mentorship to foster an environment of high-impact contributions from all team members.

Join us in creating the most dependable on-demand logistics engine for last-mile retail delivery! We are on the lookout for a seasoned machine learning engineer to aid in the development of cutting-edge growth and personalization models that will elevate DoorDash's expanding retail and grocery services.About the RoleWe are seeking a dedicated Applied Machine Learning expert to become part of our innovative team. As a Staff Machine Learning Engineer, you will conceptualize, design, implement, and validate algorithmic enhancements that enrich the growth and personalization experiences central to our rapidly evolving grocery and retail delivery business. Leveraging our advanced data and machine learning infrastructure, you will implement novel ML solutions to enhance the consumer search experience, making it more relevant, seamless, and enjoyable across grocery, convenience, and various retail sectors. A strong command of production-level machine learning and proven experience in addressing end-user challenges while collaborating effectively with multidisciplinary teams is essential.This position will report to the engineering manager on our Personalization team and is expected to be hybrid, combining both in-office and remote work (#LI-Hybrid).

Join Decagon as a Staff Software Engineer specializing in Machine Learning Infrastructure. In this role, you will play a crucial part in enhancing and optimizing our machine learning systems. You will collaborate with a talented team of engineers to build scalable and efficient infrastructure that supports our AI-driven initiatives.As a key contributor, you will leverage your expertise in software engineering and machine learning to solve complex challenges and drive innovation. Your work will impact various projects and help shape the future of our technology.

Join Whatnot as a Machine Learning Platform Engineer, where you'll play a pivotal role in shaping the future of our AI-driven solutions. In this dynamic position, you will collaborate with cross-functional teams to design, implement, and optimize machine learning platforms that drive efficiency and innovation.Your expertise will be critical in enhancing our data processing capabilities and deploying robust machine learning models at scale. If you are passionate about leveraging cutting-edge technology to solve complex challenges, we want to hear from you!

Quizlet, Inc. supports millions of learners each month by combining cognitive science with advanced machine learning. The platform serves two-thirds of U.S. high school students and half of college students, powering over 2 billion learning interactions monthly. Quizlet’s mission centers on making education more personal and effective for students, professionals, and lifelong learners. The AI & Data Platform team underpins Quizlet’s applied AI initiatives. This group develops and maintains the systems behind personalization, recommendations, the AI Coach, content generation, and emerging agentic experiences. The team oversees the full machine learning model lifecycle: data and feature engineering, training, evaluation, deployment, and inference. Reliability, speed, security, and observability guide their work. Their approach blends managed Google Cloud services, top vendor tools, open-source solutions, and custom internal abstractions to achieve efficient, reliable outcomes. Role overview The Senior Staff Engineer, AI Platform, is a senior individual contributor who defines the technical direction for Quizlet’s next generation of machine learning and large language model infrastructure. This hands-on role involves architecting core platform systems, steering build-versus-buy decisions, and collaborating with teams across Applied AI, Data Science, Product Engineering, and Infrastructure. The position sets standards for how models and LLM-driven systems are trained, evaluated, deployed, and governed at scale. This role is well suited to an engineer who excels at the senior-staff level in large organizations but values the autonomy and impact of a smaller, cloud-native setting. The technology stack includes Google Cloud, Kubernetes and GKE, distributed training, MLflow workflows, data and feature platforms, online and asynchronous inference, and the evaluation and observability tools needed to operate predictive ML and LLM systems at scale. Work location and schedule This is an onsite position based in San Francisco, CA. Team members are expected in the office at least three days a week: Monday, Wednesday, and Thursday, to foster collaboration.

tvScientific seeks a Machine Learning Platform Engineer to help shape the company’s advertising technology. This position can be based in San Francisco, CA, or performed remotely from anywhere in the United States. Role overview This role focuses on building and refining machine learning models that drive the core of tvScientific’s advertising platform. The work combines technical skill with creative problem-solving to support the platform’s effectiveness. What you will do Develop and optimize machine learning models to enhance advertising performance Collaborate with team members to deliver solutions that balance innovation, scalability, and reliability Apply technical expertise to address challenges at the intersection of technology and creative thinking Location Candidates may work from San Francisco, CA, or remotely within the US.

Join Hive as a Senior Machine Learning Engineer and help shape the future of AI! We are seeking passionate individuals who excel at developing and deploying cutting-edge deep learning models. In this role, you will work with large-scale datasets to create innovative machine learning solutions, collaborating closely with a talented team of engineers to push the boundaries of artificial intelligence. Ideal candidates will have a proven track record of building and scaling machine learning projects from conception to production, along with a strong commitment to continuous learning and personal ownership in their work.

OverviewPluralis Research is at the forefront of Protocol Learning, innovating a decentralized approach to train and deploy AI models that democratizes access beyond just well-funded corporations. By aggregating computational resources from diverse participants, we incentivize collaboration while safeguarding against centralized control of model weights, paving the way for a truly open and cooperative environment for advanced AI.We are seeking a talented Machine Learning Training Platform Engineer to design, develop, and scale the core infrastructure that powers our decentralized ML training platform. In this role, you will have ownership over essential systems including infrastructure orchestration, distributed computing, and service integration, facilitating ongoing experimentation and large-scale model training.ResponsibilitiesMulti-Cloud Infrastructure: Create resource management systems that provision and orchestrate computing resources across AWS, GCP, and Azure using infrastructure-as-code tools like Pulumi or Terraform. Manage dynamic scaling, state synchronization, and concurrent operations across hundreds of diverse nodes.Distributed Training Systems: Design fault-tolerant infrastructure for distributed machine learning, including GPU clusters, NVIDIA runtime, S3 checkpointing, large dataset management and streaming, health monitoring, and resilient retry strategies.Real-World Networking: Develop systems that simulate and manage real-world network conditions—such as bandwidth shaping, latency injection, and packet loss—while accommodating dynamic node churn and ensuring efficient data flow across workers with varying connectivity, as our training occurs on consumer nodes and non-co-located infrastructure.

About Us:At Ambience Healthcare, we aspire to redefine healthcare technology. We are creating an AI intelligence platform that brings humanity back to healthcare while delivering significant ROI for health systems nationwide.Our cutting-edge technology enables healthcare providers to concentrate on exceptional patient care by alleviating the administrative tasks that detract from their critical responsibilities. Ambience provides real-time, coding-aware documentation and clinical workflow support across various healthcare settings, including ambulatory, emergency, and inpatient environments, partnering with top health systems across North America.We are relentless in our pursuit of excellence, exhibiting extreme ownership as we develop optimal solutions for our health system partners. We value transparency, positivity, and profound insight — holding each other to high standards because the challenges we tackle are of utmost importance.Ambience has been recognized as the leading company for improving clinician experience in the KLAS Research Emerging Solutions Top 20 Report, named one of the Next Big Things in Tech by Fast Company, and selected as one of the best AI companies in healthcare by Inc. Additionally, we were honored as a LinkedIn Top Startup in 2024 and 2025. Our esteemed investors include Oak HC/FT, Andreessen Horowitz (a16z), OpenAI Startup Fund, and Kleiner Perkins — and we’re just getting started.The Role:As a Staff Machine Learning Engineer on the Frontier AI team at Ambience, you will tackle the most challenging model quality issues across our clinical AI products, including foundational coding models, adaptive scribing, voice agents, long-context chart understanding, and clinical reasoning. This role focuses on research direction, designing learning loops, and driving comprehensive improvements in model quality over time.Ambience delivers advanced clinical AI solutions in real-world healthcare environments. The models that fuel our products operate under unique constraints, including proprietary ontologies, complex electronic health record (EHR) data, stringent compliance requirements, and clinician workflows where both latency and accuracy are critical. You will leverage your deep research instincts and engineering rigor to push the boundaries of what is possible.Our engineering roles are hybrid, requiring in-office attendance at our San Francisco location three days a week.

Are you ready to push the boundaries of your potential?At Coinbase, we strive to enhance economic freedom globally. This ambitious mission requires our utmost dedication as we build the next-generation onchain platform and, with it, the future of the global financial system.We are searching for an exceptional candidate who not only shares our passion for our mission but is also excited about the transformative power of cryptocurrency and blockchain technology in revolutionizing the financial landscape. We seek individuals who are determined to make a significant impact on the world, thrive under pressure, and appreciate the privilege of collaborating with top-tier professionals. If you are someone who welcomes challenges and actively seeks growth through feedback, we want to hear from you.Our work culture is intense and may not be for everyone, but if you aspire to shape the future alongside talented individuals who hold high expectations, this is the place for you.Although many positions at Coinbase are remote-first, we are not entirely remote. We value in-person participation throughout the year, with multiple team and company-wide offsite events designed to enhance collaboration and foster connections. Attendance is encouraged and fully supported.The Core Automation Team is a dynamic and innovative group committed to creating AI infrastructure and automation solutions that boost productivity, inspire innovation, and deliver significant customer impact at Coinbase. Our goal is to streamline operations for customer support and compliance teams while ensuring outstanding customer experiences. As a Machine Learning Engineer on our team, you will play a pivotal role in this transformation, utilizing cutting-edge AI models, frameworks, and tools, along with an array of extensive data sources, including blockchain data. Your contributions will focus on dismantling barriers to cryptocurrency adoption and shaping the future of AI-driven customer interactions.

About Scale AI At Scale AI, we are dedicated to propelling the advancement of AI applications. Over the past eight years, we have established ourselves as the premier AI data foundry, supporting groundbreaking innovations in fields such as generative AI, defense technologies, and autonomous vehicles. Following our recent Series F funding round, we are intensifying our efforts to harness frontier data, paving the way toward achieving Artificial General Intelligence (AGI). Our work with enterprise clients and governments has enhanced our model evaluation capabilities, allowing us to expand our offerings for both public and private evaluations. About the ACE Team The Agent Capabilities & Environments (ACE) team, a vital part of Scale’s Research organization, unites customer-focused Researchers and Applied AI Engineers. Our primary mission is to conduct research on agent environments and reinforcement learning reward signals, benchmark autonomous agent performance in real-world contexts, and develop robust data programs aimed at enhancing the capabilities of Large Language Models (LLMs). We are committed to creating foundational tools and frameworks for evaluating models as agents, focusing on autonomous agents that interact dynamically with a wide range of external environments, including code repositories and GUI interfaces. About This Role This position sits at the cutting edge of AI research and its practical applications, concentrating on the data types necessary for the development of state-of-the-art agents, including browser and software engineering agents. The ideal candidate will investigate the data landscape required to propel intelligent and adaptable AI agents, steering the data strategy at Scale to foster innovation. This role demands not only expertise in LLM agents and planning algorithms but also creative problem-solving skills to tackle novel challenges pertaining to data, interaction, and evaluation. You will contribute to influential research publications on agents, collaborate with customer researchers, and partner with the engineering team to transform these advancements into scalable real-world solutions.

Saris AI, based in San Francisco with teams in Montreal and Toronto, develops advanced agentic AI systems for the banking industry. The company focuses on automating complex workflows that require long-context reasoning, integration with legacy systems, and strict compliance. With live AI agents already supporting real customer operations, Saris AI is expanding quickly and seeking technical leaders who want to shape the future of work in banking. Role overview This is a hands-on leadership position within the core engineering team in San Francisco. The Machine Learning Engineering Lead will guide machine learning systems from initial concept through scaling, helping define both the technical vision and the supporting infrastructure. What you will do Oversee the ML/AI function end to end, setting technical direction and standards across the company. Design and supervise development of multi-modal, agentic AI systems that power live customer workflows. Build and manage evaluation frameworks, datasets, and metrics to improve agent performance. Drive productionization of ML systems with an emphasis on reliability, scalability, and compliance. Recruit, develop, and mentor a high-performing ML team, fostering strong practices in modeling, experimentation, and deployment. Requirements 8+ years of experience in machine learning or AI engineering, including time as a technical lead or manager. Proven track record leading ML projects from concept to production deployment. Expertise with large language models (LLMs) and/or agentic systems, especially in customer-facing products. Strong grasp of ML fundamentals: deep learning, transformers, model evaluation, and trade-offs. Hands-on experience scaling ML systems in production, with a focus on monitoring, iteration, and reliability. Ability to lead engineering teams, influence architecture, and set technical direction. Comfort working in early-stage, ambiguous, and rapidly changing environments.