Note: The job is a remote job and is open to candidates in USA. Foxglove is a company focused on building the data infrastructure for robotics operating in real-world environments. They are seeking an Applied ML Engineer to design, deploy, and scale ML systems that power their data platform, focusing on production ML workloads and infrastructure optimization.
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
Skills
- 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
- 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
Benefits
- $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
Company Overview