Applied ML Engineer

Build the data infrastructure that powers robots in the real world. Robotics is moving from research labs into production fleets across factories, warehouses, vehicles, defense systems, agriculture, logistics, and field deployments. As robots scale across the physical world, every failure, regression, edge case, and unexpected behavior becomes a data problem: what happened, when, on which robot, and why? Every robot, in every industry, requires the same core capabilities: to sense, understand, and act on multimodal data from the physical world. At Foxglove, we built the agentic data platform robotics and Physical AI teams use to answer those questions. We help robotics teams make vast quantities of robot 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. Key ResponsibilitiesDeploy and operate inference infrastructure for production ML workloads, including model serving, scaling, and cost optimizationBuild and maintain vector database integrations and embedding applications to support semantic search over multimodal (image, video, point cloud, and timeseries) robotics dataDesign and implement evaluation and training infrastructure, to help us iterate quickly on model performanceOwn cloud architecture decisions and tooling that affect inference latency, throughput, cost, and reliability at scaleCollaborate with product engineers to ship application-driven ML features tailored to developers building the cutting edge of robotics and physical AI, not prototype experimentsIdentify the right off-the-shelf solutions and adapt them for production, and know when to build vs. buy What We're Looking ForStrong hands-on experience in production ML infrastructure: cloud inference, model serving optimization frameworks (e.g., TorchServe, vLLM, Triton), and cost managementExperience with the technologies used in building retrieval systems, including vector databases (e.g., Pinecone, Lance, turbopuffer, pgvector) and text-image embedding modelsSolid engineering fundamentals: distributed systems, cloud infrastructure (AWS/GCP), and production reliabilityA bias toward application and product impact over research; you're excited by shipping things that work, not writing papersProven ability to operate independently, make good tradeoffs, and move fast in a high-ownership environmentExcellent communication skills; you can explain ML tradeoffs to non-ML engineers Bonus PointsFamiliarity with fine-tuning and domain adaptation techniques for LLMs or embedding models (i.e. SFT, PEFT)Experience with data mining or hybrid search workflows, especially as applied in robotics autonomous vehicles, or physical AI workflowsExperience building ML tooling, data management, and evaluation frameworks from scratch What We Offer$300 monthly budget towards commuter benefits or building your personal workspace (remote only)Competitive equity grant in a Series B companyMedical, Dental, Vision, and Term Life insurance coverage at 100% for employees and 75% for dependents401(k) matching up to 4%4 weeks vacation, plus holidays and winter breakAll expenses paid company off-sites 2× per year Why Join UsImpact: Own growth at a fast-growing, high-leverage moment for the company.Mission: Accelerate the development of the next generation of robotics and embodied AI.Team: Work with world-class engineers, designers, and researchers passionate about open-source and developer tools.Ownership: Drive initiatives end-to-end, with high autonomy and visibility.