Job Description

TL;DR: If you: obsess over how intelligent systems learn, adapt, and improve over time; have a track record of exceptional research or engineering achievement; move effortlessly between papers, experiments, and production code; turn half-baked ideas into working systems… … you should apply for this role! About P-1 AI: At P-1 AI, we are building an AI engineer agent for the physical world named Archie. We maximize Archie’s anthropomorphism so that he fits seamlessly into existing engineering teams and workflows in the form factor of a human engineer. Archie today is at the level of a junior mechanical and electrical engineer, with a quantitative intuition over the product design space and the ability to use complex engineering tools—the same tools his human teammates use. Archie's tech stack includes a custom agentic harness, structured design representation, continual skills learning, and small custom post-trained models (SFT and RLVR) using proprietary semi-synthetic training data sets and environments which create a deep competitive moat. Our ultimate aim is to build engineering ASI. We recently announced a $50 million Series A financing led by NEA, which added former General Electric CEO Jeff Immelt to our board. The round also included the addition of several AI luminaries from Anthropic and Nominal to our existing angel investors from Google and OpenAI. About the opportunity: We’re seeking an exceptional AI Research Scientist to join our small team of elite researchers and help us push the boundaries of AI applied to the physical world. This role blends cutting-edge AI research with hands-on engineering, and is ideal for someone who thrives at the intersection of ideas and implementation. About the role: Research and develop approaches for continual learning, fast adaptation, memory formation, knowledge retention, and transfer learning in large-scale AI systems. Own the full development pipeline from data generation to evaluation to product integration. Contribute to both research strategy and technical implementation—this is a hands-on role. Have ownership over your own applied research stream. Investigate topics such as continual learning, online learning, test-time adaptation, memory architectures, meta-learning, self-improvement, and knowledge consolidation. About you: A PhD (or equivalent experience) in Computer Science, Robotics, Engineering, Math, or a related field. Have conducted research in one or more areas such as continual learning, lifelong learning, meta-learning, online learning, reinforcement learning, memory systems, model adaptation, transfer learning, or related fields. Are fluent in Python and modern ML stack such as PyTorch or JAX, and distributed training frameworks. Creative approach to bringing definition and solutions to under-specificed challenges. Are excited by fundamental questions around how intelligent systems acquire, retain, and use knowledge over time. Thrive in fast-moving, collaborative environments, and communicate technical matters with clarity. Take a high-ownership, “make it work” approach to problem solving. Publications in top-tier venues. Location: Onsite in San Mateo, CA. Relocation support available. Benefits: Competitive salary, meaningful equity ownership, healthcare, dental, vision, 401(k) match, and unlimited PTO. Interview process: Initial screening call (30 mins) Biographical/behavioural interview (60 mins) Technical interview (75 mins) CEO interview (30 mins)

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