About the role
Build the data infrastructure for robots operating in the real world. Robotics is moving from research labs into production across factories, warehouses, vehicles, and field deployments. When robots fail, behave unexpectedly, or need to be improved, engineers rely on data to understand what actually happened. At Foxglove, we build the observability, visualization, and data infrastructure that makes that possible. Our tools are used by robotics and autonomous systems teams to ingest, store, query, replay, and analyze massive volumes of multimodal sensor data from live systems and from production fleets.
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. Key
Responsibilities 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 edge of robotics and physical AI, not prototype experiments Identify the right off-the-shelf solutions and adapt them for production, and know when to build vs. buy
