Research Scientist - Applied AI

P-1 Ai•San Francisco, CA

5h•Hybrid

About The Position

About P-1 AI: We are building an engineering AGI. We founded P-1 AI with the conviction that the greatest impact of artificial intelligence will be on the built world. Our first product is Archie, an AI engineer capable of quantitative intuition over physical product domains and engineering tool use. Archie initially performs at the level of an entry-level design engineer but rapidly gets smarter and more capable. We aim to put an Archie on every engineering team at every industrial company on earth. Our founding team includes the top minds in deep learning, model-based engineering, and industries that are our customers. We closed a $23 million seed round led by Radical Ventures that includes a number of other AI and industrial luminaries (from OpenAI, DeepMind, etc.). About the role: We’re seeking an exceptional AI Research Scientist to join our small team of elite researchers (from places including MIT, Stanford, Yale, Berkeley, Princeton) 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. You’ll be leading projects that develop agentic AI systems designed to solve real-world mechanical, electrical, and aerospace engineering problems—systems that think, remember, act, and adapt. This is not a “pure research” position: we’re looking for a hacker-scientist hybrid—someone who’s published in top venues but is not afraid of any layer in the tech stack. This role is based in our San Mateo Bay Area office, with a minimum of 3 days a week in-office. We will support relocation for candidates interested in moving to the Bay Area. Why this role: Today’s AI models excel at text and code, but struggle when problems require spatial reasoning, quantitative intuition, and long-horizon planning. This role exists to close that gap.

Requirements

Have hands-on experience with LLM post-training pipelines at the frontier of AI research (IFT, RLVR, RLHF) from data generation/curation to experimentation to evaluation.
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 applied problems, especially in mechanical, electrical, or aerospace engineering.
Thrive in fast-moving, collaborative environments, and communicate technical matters with clarity.
Take a high-ownership, “make it work” approach to problem solving.

Nice To Haves

A PhD (or equivalent experience) in Computer Science, Robotics, Engineering, Math, or a related field.
Publications in top-tier venues.
Experience with multi-modal transformer architectures.
Hands-on experience in physical engineering domains.
Deep knowledge of machine learning/reinforcement learning foundations as well as its practical applications

Responsibilities

Apply (or invent) reinforcement learning strategies for reasoning and planning in long-horizon tasks in mechanical, aerospace, and electrical engineering environments.
Contribute to both research strategy and technical implementation—this is a hands-on role.
Have ownership over your own applied research stream.
Collaborate with a small, elite team of researchers and engineers across domains.
Stay on the edge of what’s possible and bring promising ideas into reality (e.g. following the literature, attending conferences, etc)