Applied ML Engineer

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 robotics data
• Design and implement evaluation and training infrastructure to help iterate quickly on model performance
• Own cloud architecture decisions and tooling affecting inference latency, throughput, cost, and reliability
• Collaborate with product engineers to ship application-driven ML features
• Identify and adapt off-the-shelf solutions for production use

Requirements

• Strong hands-on experience in production ML infrastructure (cloud inference, model serving optimization frameworks like TorchServe, vLLM, or Triton)
• Experience with vector databases (e.g., Pinecone, Lance, turbopuffer, pgvector) and text-image embedding models
• Solid engineering fundamentals in distributed systems and cloud infrastructure (AWS/GCP)
• Proven ability to operate independently and make technical tradeoffs
• Excellent communication skills for explaining ML tradeoffs to non-ML engineers

Preferred Qualifications

• Familiarity with fine-tuning and domain adaptation techniques for LLMs or embedding models (SFT, PEFT)
• Experience with data mining or hybrid search workflows in robotics or autonomous vehicles
• Experience building ML tooling, data management, and evaluation frameworks from scratch

About the Company

Foxglove is the data platform for Physical AI. Built for robotics teams developing real-world systems, Foxglove provides a purpose-built, modular platform to collect, organize, and learn from vast quantities of multimodal data.