--- title: 'Robot Operator at Physical Intelligence' canonical: 'https://feeny.ai/job/robot-operator-physical-intelligence-san-francisco-gq9jsnxd3s5g' type: 'job' last_seen: '2026-09-06' --- # Robot Operator at Physical Intelligence - **Company:** Physical Intelligence - **Location:** San Francisco, CA - **Compensation:** $25/hr - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-08-06 - **Last confirmed live:** 2026-09-06 - **Apply:** https://jobs.ashbyhq.com/physicalintelligence/eb3ed968-630f-429a-9e44-5508b77d554b/application **Skills:** Robotic Control Systems, Hand-eye Coordination, Manual Dexterity, Data Collection, Video Annotation, Robotics Systems, Automation, AI/ML Concepts, Gaming Controllers, Simulation Experience > As a Robot Operator, you will teleoperate robotic arms and guide robots to perform diverse physical tasks, such as household chores and assembly work. You will collect high-quality demonstration data to train AI systems for general-purpose robotics, ensuring high standards for data quality and consistency. ## Job description ## ABOUT PHYSICAL INTELLIGENCE Physical Intelligence is building the future where AI-powered robots seamlessly integrate into our daily lives. Imagine a robot that can fold your laundry, prepare meals, and organize your space. Picture robots in warehouses that can handle any package, or manufacturing robots that can adapt to new products without reprogramming. We're making this vision reality by developing general-purpose AI that can control robots to perform any physical task. Our team of engineers, scientists, and roboticists is creating foundation models—the same breakthrough technology behind ChatGPT—but for the physical world. Just as language models learned to understand and generate text from massive datasets, our robots learn to interact with the physical world through high-quality demonstration data. ## THE ROLE Data collection is the fuel that drives our mission. Every robot movement, every successful task completion, every demonstration you provide teaches our AI systems how to interact with the physical world. As a Robot Operator, you're not just controlling robots—you're literally training the AI that will power the next generation of intelligent machines. You'll be at the forefront of robotics AI, working hands-on with cutting-edge robotic systems to generate the high-quality training data our models need. Your precise demonstrations teach our AI everything from delicate manipulation tasks to complex multi-step processes. This is your chance to directly contribute to technology that will transform how robots help humans in homes, workplaces, and beyond. ## WHAT YOU'LL DO Primary Responsibilities - Teleoperate robotic arms through a variety of tasks using our intuitive control systems - Either lead robot movements with your arms (the robot mirrors your actions) or guide robots using specialized controllers - Complete diverse tasks ranging from household activities like folding laundry to complex assembly work - Maintain high standards for data quality and consistency across all demonstrations - Meet established metrics for data collection volume and quality during your shift Important Note: This is a metrics-based role where you'll be expected to meet specific data collection goals throughout your shift. The work involves repetitive task execution, and the quality of data collection is extremely important to our AI training success. Watch some examples of training here: https://drive.google.com/drive/u/0/folders/1YPNYJxKF4i2U41o9pi5AzHCQFYaf2dnw https://drive.google.com/drive/u/0/folders/1YPNYJxKF4i2U41o9pi5AzHCQFYaf2dnw Example Tasks You'll Train Robots On - Picking up grocery items and placing them in shipping bags - Sorting cups, plates, and utensils into bins - Opening and closing jars of various sizes - Folding different types of clothing and fabrics - Installing light bulbs and other simple assembly tasks - Multi-step electronics assembly processes Additional Duties - Review and annotate videos of robot task performances using computer interfaces - Provide detailed feedback on robot performance and data quality - Assist with equipment setup and basic office tasks as needed - Participate in process improvements to enhance data collection efficiency Physical Requirements - Ability to stand at a workstation for 8-hour shifts - Full use of both arms and hands for robot control - Good hand-eye coordination and manual dexterity - Attention to detail for quality control ## WORK ENVIRONMENT & SCHEDULE Shift Options (8 hours with 30-minute lunch + two paid breaks): - Morning: 8:00 AM - 4:00 PM PT - Evening: 4:00 PM - 12:00 AM PT - Overnight: 12:00 AM -8:00 AM PT Shift patterns: Mon-Fri, Wed-Sun, Sat-Wed Commitment: Minimum 5 days per week Compensation: $25/hour + benefits package ## WHAT WE'RE LOOKING FOR Ideal Background - Experience with hands-on technical work, lab environments, or precision tasks - Interest in AI, robotics, and cutting-edge technology - Strong attention to detail and quality focus ## Key Qualities - Meticulous attention to detail—data quality is crucial for AI training - Good manual dexterity and hand-eye coordination - Enjoys repetitive, precision-focused work - Thrives in fast-paced, metrics-driven environments - Excited about contributing to breakthrough AI research - Collaborative mindset and strong work ethic ## Nice to Have - Experience with robotics systems or automation - Background in manufacturing, assembly, or laboratory work - Familiarity with AI/ML concepts - Gaming or simulation experience with controllers ## WHY THIS ROLE MATTERS You'll be part of the team building the foundation for general-purpose robotics AI. Every demonstration you provide directly impacts our ability to create robots that can help with household chores, assist in workplaces, and improve quality of life. This is a rare opportunity to work at the cutting edge of AI and robotics while developing valuable technical skills in a rapidly growing field. Ready to help train the robots of tomorrow? We'd love to connect with you! 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 - [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