Applied ML Engineer

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.