Physical AI Researcher
CAJobPosted 6 days agoStill listed today
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Job overview
Feather Robotics is seeking a Physical AI Researcher to advance reliability and data efficiency for robotic manipulation and support commercial deployment. The researcher will develop and deploy models for general-purpose robots, conduct applied research, and collaborate with robotics engineers to integrate perception, control, and cognition. The role offers ownership of the research stack and the opportunity to help architect the company’s humanoid platform.
Skills & qualifications
Skills
Qualifications
Full job description
About the job We're looking for a Physical AI Researcher passionate about redefining the foundation of labor through robotics. You'll help lead the research effort to solve some of the biggest hurdles in robotics: reliability and data efficiency for manipulation. Supported by our humanoid platform that has been shipping for the past year, you will do applied research that brings Physical AI ever closer to mainstream commercial deployment.
About Feather Feather is a VC-backed startup assembling a world-class team to build general-purpose robots. While competitors pursue end-to-end integration, we are focused on creating the best labor platform on which others will build, monetize, and scale, fostering an ecosystem of solutions akin to Apple’s or Nvidia’s.
We’ve moved through the hardware lifecycle at lightspeed -- building our first prototype in two months, fulfilling our first order in nine months (vs. 31 months for Figure), and working with a $4B revenue manufacturer. Our units are deployed across several fields today, including cooking, retail, manufacturing, and more.
Responsibilities
- Develop, train, and deploy machine learning models for general-purpose robots
- Implement and optimize algorithms in areas such as imitation learning, RL, diffusion policies, VLA, and navigation
- Collaborate with robotics engineers to integrate perception, control, and cognition into a unified system
- Conduct applied research that pushes Physical AI forward, with an emphasis on commercial viability
- Analyze performance, debug complex behaviors, and continuously improve model robustness in the real world
Qualifications
- MS or PhD in EECS, Robotics, CS, AI, or a related field, or equivalent industry experience
- Strong background in machine learning, with applied work in robotics or embodied AI
- Experience with one or more of: Vision-Language Models (VLAs), imitation learning, diffusion policies, reinforcement learning, navigation
- Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow)
- Track record of impactful projects, whether in academia, open-source, or industry
- Publications in top robotics conferences (ICRA, CoRL, RSS, IROS, etc.)
Why Join Us
- Own the whole research stack and be one of the principal architects of our humanoid platform instead of only working on a small fragment
- Shape the future of robotics and physical AI in a tight-knit, driven team that values ownership, speed, and creativity
- We have the capital, the hardware ownership, and the generational upside to make this your life's work
How this job compares
Set against the 583 open Machine Learning Researcher jobs on Olive Jobs, counted November 12, 2026.
- Pay
- This postingNot listed
- California median$184–291K/yr147 salary ranges
- US median$39–85/hr22 hourly ranges
- Openings
- Olive Jobs lists 263 open Machine Learning Researcher jobs in California and 583 across the US. 6% of them nationwide are listed as remote or flexible.
- Experience
- Machine Learning Researcher postings that state a minimum ask for 3 years of experience at the median; the middle half ask for 2 to 5 years (224 postings).
- Education
- 241 of the 583 open Machine Learning Researcher jobs (41%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 12, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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