# Applied ML Engineer
**Company:** [Foxglove](https://hotfix.jobs/companies/foxglove)
**Location:** San Francisco, CA
**Salary:** $183K-$275K
**Skills:** Torchserve, vLLM, Triton, Pinecone, Lance, Pgvector, AWS, GCP, Distributed Systems, LLMs, Peft, Sft
**Posted:** 2026-04-06
> Designs, deploys, and scales ML infrastructure for production robotics data platform, including inference pipelines, vector databases for semantic search on multimodal data, and training/evaluation workflows. Requires hands-on experience with model serving, cloud infra, and retrieval systems.
## Job Description
## 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
**Apply:** https://hotfix.jobs/jobs/applied-ml-engineer-at-foxglove-2d222c59-b528-46dc-a316-2adafdb48606
**Canonical:** https://hotfix.jobs/jobs/applied-ml-engineer-at-foxglove-2d222c59-b528-46dc-a316-2adafdb48606