---
title: 'Production Test Engineer at Physical Intelligence'
canonical: 'https://feeny.ai/job/production-test-engineer-physical-intelligence-san-francisco-8ngeahchx0hx'
type: 'job'
last_seen: '2026-09-06'
---

# Production Test Engineer at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-03
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/81d1eb25-ada9-40fd-8708-e1e42ecb2b7e

## Job description

Physical Intelligence is bringing general-purpose AI into the physical world. We are a team of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically actuated devices of the future.

We’re looking for a Production Test Lead to build the software foundation for manufacturing Physical Intelligence’s robotic systems at increasing volume.

You’ll own the systems that technicians use to flash firmware, provision devices, configure hardware, execute calibration and test procedures, and record manufacturing results. You’ll translate evolving engineering processes into safe, guided workflows that can be executed consistently without requiring technicians to use a command line or understand the underlying implementation.

This role sits at the intersection of runtime software, hardware engineering, manufacturing, test, infrastructure, and inventory systems. You will work closely with those teams to turn prototype scripts and manual procedures into secure, observable, versioned production tooling.

In this role you will

- Own the architecture and development of PI’s manufacturing software platform.
- Build technician-friendly, step-by-step workflows for assembly, flashing, provisioning, calibration, and final test.
- Convert engineering scripts and manual procedures into versioned, repeatable manufacturing operations.
- Develop local station software that securely exposes allowlisted hardware capabilities to hosted manufacturing workflows.
- Design workflow primitives for automated steps, operator instructions, retries, timeouts, cancellation, resumability, and acceptance criteria.
- Build reliable mechanisms for downloading, caching, verifying, and updating firmware and manufacturing binaries.
- Integrate manufacturing workflows with inventory systems to register hardware, assign identities, and record required metadata.
- Establish traceability for operators, stations, devices, software versions, step results, durations, and captured test artifacts.
- Debug problems across cloud services, local Linux stations, networks, embedded devices, and test equipment.
- Partner with manufacturing and hardware engineers to define acceptance criteria and automate quality checks.

## What we hope you'll bring

- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field, or equivalent practical experience.
- 5+ years of software engineering experience, preferably involving manufacturing, robotics, hardware test, embedded systems, or fleet operations.
- Strong Python experience and familiarity with Linux systems, services, permissions, packaging, and process management.
- Experience building reliable web applications and APIs used to orchestrate local or remote hardware.
- Experience designing stateful workflows with retries, timeouts, resumability, cancellation, and failure recovery.
- Familiarity with device provisioning, firmware flashing, OTA updates, calibration, or automated hardware testing.
- Experience integrating with inventory, traceability, MES, ERP, or similar production systems.
- Strong understanding of asynchronous jobs, idempotency, durable state, and distributed-system failure modes.
- Experience with secure artifact distribution, authentication, authorization, and least-privilege hardware access.
- Ability to diagnose issues across browsers, networks, operating systems, backend services, and physical devices.
- Strong product judgment for technician-facing workflows and operational tooling.
- Excellent cross-functional communication and project-management skills.

Bonus points if you have

- Experience with robotics, computer vision, camera calibration, networking hardware, Raspberry Pi-class devices, Bazel, Kubernetes, or cloud infrastructure.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

## About Physical Intelligence

## Company Overview
- **One-liner**: Physical Intelligence is building general-purpose AI foundation models that can control any robot to perform any physical task, aiming to bring the flexibility of large language models into the physical world.
- **Entity Type**: Private (Series A/B stage; total funding $1.07B across multiple rounds)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Sergey Levine (UC Berkeley), Chelsea Finn (Stanford), Karol Hausman (CEO, ex-Google DeepMind), Lachy Groom (ex-Stripe), Quan Vuong (ex-Google DeepMind), Brian Ichter, and Adnan Esmail

## Core Business
- **Primary industry**: Robotics foundation models / Artificial Intelligence / Research Services
- **Target customers**: B2B enterprise – logistics, grocery, manufacturing, and other verticals requiring physical automation (currently testing with a small number of partners)
- **Mission**: “Bringing general-purpose AI into the physical world” – developing learning algorithms and foundation models to power robots and other physically-actuated devices

## Products & Services
- **π0 (pi-zero)**: First generalist policy released in October 2024; a vision-language-action (VLA) model capable of dexterous multi-task robot control. Open-sourced in February 2025.
- **π0.5**: VLA with open-world generalization (April 2025); can control a mobile manipulator to clean unfamiliar kitchens or bedrooms.
- **π0.7**: Steerable robotic foundation model with emergent capabilities (April 2026); can control a new robot platform without additional training.
- **π*0.6**: VLA trained with reinforcement learning to improve success rate and throughput on real-world tasks (November 2025).
- **FAST**: Efficient robot action tokenizer enabling 5x faster training of generalist policies (January 2025).
- **Multi-Scale Embodied Memory (MEM)**: Gives models long- and short-term memory for tasks exceeding ten minutes (March 2026).
- All offerings are research-stage models; the company does not currently sell a commercial product but partners with select companies for testing.

## Market Standing
- **Valuation**: $5.6 billion (as of January 2026, per TechCrunch)
- **Total Funding**: $1.07 billion across four rounds:
  - Seed (March 2024): $70M led by Lux Capital and Thrive Capital
  - Series A (November 2024): $400M led by Lux Capital and Thrive Capital
  - Venture Round (November 2025): $600M from 8 investors
  - Venture Round (March 2026): undisclosed amount from 4 investors
- **Notable Investors/Partners**: Khosla Ventures, Lux Capital, Thrive Capital, Sequoia Capital, OpenAI, CapitalG, Redpoint Ventures, Bond
- **Growth Signals**: Headcount grew 183.5% year-over-year to 163 employees (LinkedIn, mid-2026); the company states it has “blown through” its original 5-10 year roadmap in just 18 months; test deployments in logistics, grocery, and food production.

## Competitive Advantages
- **Cross-embodiment learning**: Models can transfer knowledge to any new robot hardware without starting data collection from scratch, lowering the marginal cost of onboarding autonomy.
- **Pure research focus**: Unlike competitors (e.g., Skild AI), Physical Intelligence deliberately avoids near-term commercialization, allowing the team to pursue general intelligence without product pressure.
- **World-class founding team**: Combination of top robotics academics (Levine, Finn) and experienced entrepreneurs/operators (Groom, Hausman).
- **Open-source release**: π0 weights and code are publicly available, fostering community contributions and accelerating research.

## Strategic Focus
- Current priorities include scaling foundation models to more tasks and environments, improving generalization and robustness, and expanding partnerships for real-world testing.
- The company explicitly does not give investors a timeline for monetization; instead it focuses on building general-purpose physical intelligence with a 5-10 year horizon (though progress has been faster than expected).
- Future strategy centers on continuous improvement via a loop of data collection → training → evaluation → more data, rather than rapid deployment.

## Why Work Here
- **Culture**: Described as a “pure company” – internally driven by research needs, not external market demands. Highly collaborative, with a mix of engineers, scientists, and roboticists working together.
- **Work Environment**: Hybrid/in-office – most roles require on-site presence in San Francisco (396 Treat Ave), though some remote flexibility exists. In-office setting with a “no reception” vibe, open lab space with robot stations.
- **Growth**: Team has scaled from ~80 (Jan 2026) to 163 (mid-2026) and is still hiring across research, ML infra, hardware, and engineering roles. Plans to grow “as slowly as possible” to maintain quality.
- **Perks & Engineering Highlights**: Access to cutting-edge robotics hardware and compute resources; opportunity to publish research and open-source code; exposure to a wide variety of real-world automation challenges (e.g., robots learning to fold pants, peel vegetables, make espresso). Employees come from top institutions like Berkeley, Stanford, Google, NVIDIA, and Anduril.

## Sources
1. [physicalintelligence.company](https://www.physicalintelligence.company/) – Official website, model releases, and values
2. [TechCrunch](https://techcrunch.com/2026/01/30/physical-intelligence-stripe-veteran-lachy-grooms-latest-bet-is-building-silicon-valleys-buzziest-robot-brains/) – In-depth profile including valuation, strategy, and culture
3. [LinkedIn](https://www.linkedin.com/company/physical-intelligence) – Company details, headcount, funding, and talent sources
4. [Built In](https://builtin.com/company/physical-intelligence) – Career page, employee count, and office policy
5. [jobs.ashbyhq.com/physicalintelligence](https://jobs.ashbyhq.com/physicalintelligence) – Current job openings

## Other roles at Physical Intelligence

- [ML Infra Engineer, Data Systems](https://feeny.ai/job/ml-infra-engineer-data-systems-physical-intelligence-san-francisco-8g386zwjvd64) — San Francisco, CA
- [ML Infra Engineer, Modeling](https://feeny.ai/job/ml-infra-engineer-modeling-physical-intelligence-san-francisco-dt7e182daxxa) — San Francisco, CA
- [Software Engineer, Data Quality](https://feeny.ai/job/software-engineer-data-quality-physical-intelligence-san-francisco-p7f1n4g3j35e) — San Francisco, CA
- [Shift Lead](https://feeny.ai/job/shift-lead-physical-intelligence-san-francisco-23rb2r5nr82f) — San Francisco, CA
- [Fullstack Software Engineer](https://feeny.ai/job/fullstack-software-engineer-physical-intelligence-san-francisco-4d81qeq8e7hy) — San Francisco, CA
- [People Ops](https://feeny.ai/job/people-ops-physical-intelligence-san-francisco-k10k2h0mgp8j) — San Francisco, CA
- [NPI Technical Program Manager](https://feeny.ai/job/npi-technical-program-manager-physical-intelligence-san-francisco-8grzx6hq5pm4) — San Francisco, CA
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- [Supply Chain Lead](https://feeny.ai/job/supply-chain-lead-physical-intelligence-san-francisco-9n0bq8qkcvzm) — San Francisco, CA