Applied ML Engineer

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

What We're Looking For

• Strong hands-on experience in production ML infrastructure: cloud inference, model serving optimization frameworks (e.g., TorchServe, vLLM, Triton), and cost management

• Experience with the technologies used in building retrieval systems, including vector databases (e.g., Pinecone, Lance, turbopuffer, pgvector) and text-image embedding models

• Solid engineering fundamentals: distributed systems, cloud infrastructure (AWS/GCP), and production reliability

• A bias toward application and product impact over research; you’re excited by shipping things that work, not writing papers

• Proven ability to operate independently, make good tradeoffs, and move fast in a high-ownership environment

• Excellent communication skills; you can explain ML tradeoffs to non-ML engineers

Bonus Points

• Familiarity 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 workflows

• Experience 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 company

• Medical, Dental, Vision, and Term Life insurance coverage at 100% for employees and 75% for dependents

• 401(k) matching up to 4%

• 4 weeks vacation, plus holidays and winter break

• All expenses paid company off-sites 2× per year

• Impact: 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.