--- title: 'Software Engineer, Data Quality at Physical Intelligence' canonical: 'https://feeny.ai/job/software-engineer-data-quality-physical-intelligence-san-francisco-p7f1n4g3j35e' type: 'job' last_seen: '2026-09-06' --- # Software Engineer, Data Quality at Physical Intelligence - **Company:** Physical Intelligence - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-08-25 - **Last confirmed live:** 2026-09-06 - **Apply:** https://jobs.ashbyhq.com/physicalintelligence/c01b0e18-5eb8-4255-a10a-7e9a171676b7 ## Job description Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing learning algorithms to create a model that will control any robot to do any task. The Team Training robot foundation models takes enormous amounts of multimodal data. The Data Operations team makes sure that data is high quality, well understood, and safe to train on. This role owns quality assurance and audit for data, working closely with the researchers who consume it and the teams who create it. In This Role You Will - Own external data QA & audit: Build and run the end-to-end QA process for multimodal data ingested from third-party vendors and partners — sampling strategy, review workflows, pass/fail decisions, and audit trails — so data is verified before it reaches training. - Define quality standards: Partner with researchers to translate what "good data" means into concrete, measurable rubrics and acceptance criteria; keep them versioned and current as research needs evolve. - Build the system: Build the software interfaces, tooling, and infrastructure scale the system in an automated way - correctly sampling the right data for QA, internal QA checks, tracking dips in data quality from vendors and exposing those insights back to the vendors. - Manage vendor quality loops: Deliver structured, actionable feedback to vendors; coach them on standing up QA checks on their own side; iterate until vendor-side and internal QA agree; enforce standards through review and acceptance processes. ## What you'll bring - ~5 years of experience across building customer facing software. - Vendor management experience: working directly with third-party data providers, delivering actionable feedback, and measurably improving the quality of their output. - Strong ops acumen and drive — a bias to instrument, measure, and iterate quickly rather than crank hours at a broken process. - Comfort being hands-on in the data: reviewing samples yourself, writing and refining rubrics, and spot-checking your own program's output. - Clear written and verbal communication across researchers, operations teams, and external partners. ## Nice to have - Experience with robotics, egocentric video, or other multimodal sensor data. - Experience defining requirements for QA/validation tooling or working with engineers on automated data quality checks. 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 - [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 - [Robot Operator](https://feeny.ai/job/robot-operator-physical-intelligence-san-francisco-gq9jsnxd3s5g) — San Francisco, CA - [Manufacturing Engineer](https://feeny.ai/job/manufacturing-engineer-physical-intelligence-fremont-30v59z9v8ze2) — Fremont, CA - [Production Test Engineer](https://feeny.ai/job/production-test-engineer-physical-intelligence-san-francisco-8ngeahchx0hx) — San Francisco, CA - [Supply Chain Lead](https://feeny.ai/job/supply-chain-lead-physical-intelligence-san-francisco-9n0bq8qkcvzm) — San Francisco, CA