AI Research Scientist/Manager - Adaptive Intelligence

P-1 AI•San Francisco, CA

3d•$200,000 - $385,000•Onsite

About The Position

P-1 AI is building an AI engineer agent for the physical world named Archie, designed to integrate seamlessly into existing engineering teams. Archie currently functions at the level of a junior mechanical and electrical engineer, possessing quantitative intuition and the ability to use complex engineering tools. The company's technology stack includes a custom agentic harness, structured design representation, continual skills learning, and custom-trained models using proprietary data. The ultimate goal is to develop engineering Artificial Superintelligence (ASI). P-1 AI is supported by prominent venture investors and AI luminaries. This opportunity is for an exceptional AI Research Scientist/Manager to join a team of elite researchers focused on advancing AI for the physical world. The role combines cutting-edge AI research with practical engineering, appealing to individuals who excel at bridging conceptual ideas with tangible implementation.

Requirements

A PhD (or equivalent experience) in Computer Science, Robotics, Engineering, Math, or a related field.
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.
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-specified challenges.
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.

Responsibilities

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.
Have ownership over an applied research stream.
Investigate topics such as continual learning, online learning, test-time adaptation, memory architectures, meta-learning, self-improvement, and knowledge consolidation.