--- title: 'Learning & Development Lead, Operations at Physical Intelligence' canonical: 'https://feeny.ai/job/learning-development-lead-operations-physical-intelligence-san-francisco-rjw9n41fwmh4' type: 'job' last_seen: '2026-10-18' --- # Learning & Development Lead, Operations at Physical Intelligence - **Company:** Physical Intelligence - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-10-15 - **Last confirmed live:** 2026-10-18 - **Apply:** https://jobs.ashbyhq.com/physicalintelligence/c6dd5186-089c-4b70-bb88-f08d96541b0c ## Job description Learning & Development Lead — Operations ## Role Overview The Learning & Development Lead owns training and readiness for Physical Intelligence's robot operator workforce. This person is responsible for designing and running the systems that take a new operator from candidate work trial through their first 30–60 days, with the goal of getting people productive quickly while maintaining a high quality bar. They also own ongoing training as the work changes. When researchers introduce new robot workflows, tasks, tools, or operating procedures, the Learning & Development Lead translates those changes into training that can be rolled out quickly and consistently across the operation. This is a highly operational training role. We are looking for someone who has built or owned training programs for hourly or frontline employees and knows how to turn complex work into a simple, repeatable learning process. ## Responsibilities Operator Trials & Readiness - Own the operator work-trial program and ensure candidates are evaluated consistently. - Maintain clear trial standards, evaluator guidance, and scoring criteria. - Partner with recruiting and operations to continuously improve what the trial predicts about future operator success. New-Hire Onboarding - Build and run onboarding from day one through the first 30–60 days. - Define what an operator should know and be able to do at each stage of ramp. - Create training plans, checklists, certification steps, and readiness milestones. - Identify struggling operators early and create clear paths for remediation. - Continuously improve time-to-productivity for new hires. New Workflow Training - Partner with researchers and operations teams when new robot tasks, systems, or processes are introduced. - Learn new workflows quickly and translate them into clear training materials and operating instructions. - Design the rollout plan needed to train a large operator population quickly. - Track who has been trained and verify that the training actually resulted in correct execution. - Maintain consistency as training expands across shifts, teams, and locations. Training Operations - Maintain a clear training calendar and ensure training happens when required. - Coordinate trainers, operators, shift leads, and researchers. - Keep training materials current as workflows change. - Build repeatable processes so training does not depend on tribal knowledge. - Track training completion, readiness, and performance metrics. Continuous Improvement - Use operator performance data and feedback to identify gaps in training. - Work with operations leaders to understand recurring quality or productivity issues. - Determine whether problems are caused by training, instructions, process, tooling, or something else. - Continuously simplify and improve how operators learn. ## What We're Looking For - Experience owning training, onboarding, or workforce development programs for hourly or frontline employees. - Demonstrated ability to get new employees productive quickly. - Experience building structured ramp programs with clear milestones and standards. - Strong operational organization and follow-through. - Able to turn ambiguous or technical workflows into simple, teachable processes. - Comfortable managing trainers, who are responsible for training large groups of people on new procedures under tight timelines. - Strong written and verbal communication. - Able to work effectively with both highly technical stakeholders, trainers, and frontline operators. - Comfortable in an environment where processes and requirements change frequently. - Uses data and observation to improve training rather than relying only on subjective feedback. Experience in robotics is not required. 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 - [General Manager, Research Operations](https://feeny.ai/job/general-manager-research-operations-physical-intelligence-san-francisco-t2n6gysx5vvr) — San Francisco, CA - [Recruiter, Hardware](https://feeny.ai/job/recruiter-hardware-physical-intelligence-san-francisco-4n292t91sd6v) — San Francisco, CA - [Set Team Specialist](https://feeny.ai/job/set-team-specialist-physical-intelligence-san-francisco-a1zm1e3dtf2d) — San Francisco, CA - [ML Engineer - API Platform](https://feeny.ai/job/ml-engineer-api-platform-physical-intelligence-san-francisco-9rgmerynps3m) — San Francisco, CA - [ML Infra Engineer, Platform](https://feeny.ai/job/ml-infra-engineer-platform-physical-intelligence-san-francisco-r5dzsq99bg6e) — San Francisco, CA - [ML Engineer - API Platform](https://feeny.ai/job/ml-engineer-api-platform-physical-intelligence-san-francisco-ze5fj5gk238m) — San Francisco, CA - [Annotations Portfolio Lead](https://feeny.ai/job/annotations-portfolio-lead-physical-intelligence-san-francisco-v5hg1kq15xc4) — San Francisco, CA - [Software Engineer, Robot Interfaces](https://feeny.ai/job/software-engineer-robot-interfaces-physical-intelligence-san-francisco-r7xp0by9w54z) — San Francisco, CA - [Mechatronics Intern](https://feeny.ai/job/mechatronics-intern-physical-intelligence-san-francisco-nz7d9xzh1v4y) — San Francisco, CA - [Recruiter, Operations](https://feeny.ai/job/recruiter-operations-physical-intelligence-san-francisco-ctaaat19b1vv) — San Francisco, CA