Member of Technical Staff - Product Engineering

Physical Intelligence • San Francisco, CA

3d • Onsite

About The Position

The Product Engineering team builds the platform that lets other companies use PI's models: access to our models and the services around them, so a robotics company can build on PI the way developers build on API-based LLMs. See The Physical Intelligence Layer. Concretely, you own the product surface our partners touch: they send us data, get a model back, and run it. That surface is remote inference and the inference client, partner data ingestion and APIs, and deployment integrations. You take a new partner from raw data to a deployed, evaluated model with little hand-holding.

Requirements

• An exceptional generalist software engineer who ships fast and owns results end to end.
• Strong backend and systems design instincts.
• Understand how to run inference with our models.
• Do your best work directly with partners and researchers.
• Strong engineering skills: clean Python, the ability to interface with infrastructure, and sharp debugging instincts.
• Strong backend and systems design: you can design a scalable system (databases, caching, APIs, services) and defend it under scrutiny.
• Enough ML to run inference: you understand how to deploy, serve, and debug our models, even if you do not train them.
• A practical, ownership mindset: you are motivated by making things work end to end.
• Clear communication with researchers, operators, and partners.
• Comfort with ambiguity and with on-site, embedded partner work.

Nice To Haves

• Founded or worked at an early-stage robotics, AV, or infrastructure startup.
• Low-latency and real-time networking experience (inference transport, streaming, QUIC or websockets).
• Experience with robot manipulation platforms, VLAs, or other ML models.
• Familiarity with our stack: Python, Postgres, ClickHouse, GCP, Kubernetes, Modal, React and TypeScript.

Responsibilities

• Build the platform that lets other companies use PI's models: give partners access to our models, fine-tuning, remote inference, and the services around them, so a robotics company can build on PI the way developers build on API-based LLMs. This spans data ingestion and APIs, a partner portal, and deployment integrations, all working end to end and self-serve.
• Ingest partner data end to end: take a new data or robot partner from their first sample to featurized, validated data in our system, and to a checkpoint they can eval.
• Deploy and serve partner models: stand up remote inference endpoints, validate them, and get partners running our policies at low latency in their own environment.
• Be the engineer embedded in partner engagements: sit in the partner channel, debug their deployment across the full stack, unblock them, and translate what they need into what we build.
• Write production-quality code that interfaces with PI's infrastructure.
• Bridge research and partners: turn research advances into deployable systems, and surface real-world failure modes back to researchers and engineers.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed