Its values, its interview process and the questions it asks, in short episodes. Listen on a walk, then practice.
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The behavioral questions candidates should expect at Physical Intelligence cluster around ownership, hardware-plus-software debugging, data quality, reliability and researcher-engineer collaboration. This episode works through those themes with STAR guidance and builds a "why PI" answer grounded in the company's actual 2026 work.
There is almost no Glassdoor or Blind data on Physical Intelligence's interviews, so this episode separates confirmed signals (job postings, hiring pages, process patterns from guides) from inference. It covers the likely stages, the research-versus-engineering split, coding expectations, referrals and compensation realities.
Physical Intelligence's public voice is unusually candid: blog posts admit hardware limits, researchers blame their own prompting, and the CTO-level founders hedge their claims. This episode shows how that norm shapes behavioral interviews and how to tell failure and learning stories the PI way.
About 80 people, no reception desk, a test kitchen for robots and a co-founder who wants to grow "as slowly as possible." This episode draws on a first-hand TechCrunch visit and founder interviews to describe how PI works day to day, who leads it and what that means for how you present yourself.
openpi has 13,000+ GitHub stars, the team prefers real-world data over simulation, and Lachy Groom calls hardware "really hard." This episode covers the stack, the data strategy and the open-source posture so candidates can speak to PI's engineering culture with specifics.
Physical Intelligence's recent research is dominated by one theme: a demo is not a deployment. This episode covers Recap and π*0.6, RL Tokens, Multi-scale Embodied Memory and the Robot Olympics, and explains how to show the same reliability mindset in interview stories.
Physical Intelligence's research blog is the clearest public window into what the team values technically. This episode walks the model lineage from π0's flow-matching action expert through π0.5's open-world generalization to π0.7's steerable prompting, and shows how to discuss these design choices in technical and behavioral rounds.
Physical Intelligence went from a $2.4B valuation in late 2024 to reported talks at $20B in October 2026 while refusing to give investors a commercialization timeline. This episode maps the funding history, the Skild AI and Figure comparison, and how to discuss strategy and risk like an insider.
In 2026 Physical Intelligence shifted from publishing models to shipping a hosted fine-tuning and inference API used by partners from laundromats to autonomous wheel loaders. This episode covers the Feb 2026 "Physical Intelligence Layer" post, the Oct 2026 API beta, and the partner data a candidate should know cold.
Physical Intelligence is betting that robotics has always been bottlenecked by intelligence, not hardware, and that one foundation model can drive any robot. This episode unpacks the founding thesis, the π0-to-π0.7 arc, and how to speak about the mission credibly in an interview.