Applied ML Engineer

BUILD THE DATA INFRASTRUCTURE THAT POWERS PHYSICAL AI. Physical AI is moving from research labs into production fleets across industries. As robots scale across the real world, from factories to vehicles, to defense - every workflow from product development to deployment becomes a data problem: what happened, when, on which robot, and why? At Foxglove, we built the unified data platform for physical AI that developer and engineering teams use to answer those questions. We help teams make vast quantities of robotics data actionable, creating the data flywheel they need to develop, test, train, deploy, and operate robots with confidence. ABOUT THE ROLE We're looking for an Applied ML engineer with deep infrastructure instincts to help design, deploy, and scale the ML systems that power Foxglove's data platform. In this role, you'll own the infrastructure that makes ML work in production: from optimizing inference pipeline throughput to standing up training and eval workflows. You'll work directly on the problems that matter right now: retrieval applications over petabyte-scale multimodal robotics data, using the latest models to build high-performance search and data mining products, and creating the internal ML flywheel that lets us iterate fast. This is a hands-on application-driven role, not research. WHAT YOU'LL DO - Deploy and operate inference infrastructure for production ML workloads, including model serving, scaling, and cost optimization - Build and maintain vector database integrations and embedding applications to support semantic search over multimodal (image, video, point cloud, and timeseries) robotics data - Design and implement evaluation and training infrastructure, to help us iterate quickly on model performance - Own cloud architecture decisions and tooling that affect inference latency, throughput, cost, and reliability at scale - Collaborate with product engineers to ship application-driven ML features tailored to developers building the cutting