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About the Role
This role blends cutting-edge AI research with hands-on engineering, ideal for someone who thrives at the intersection of ideas and implementation. You will research and develop approaches for continual learning, fast adaptation, memory formation, knowledge retention, and transfer learning in large-scale AI systems. You will own the full development pipeline from data generation to evaluation to product integration, contributing to both research strategy and technical implementation with ownership over your own applied research stream. You will investigate topics such as continual learning, online learning, test-time adaptation, memory architectures, meta-learning, self-improvement, and knowledge consolidation.
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—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.
Requirements
- 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.
Skills
- Continual learning
- Fast adaptation
- Memory formation
- Knowledge retention
- Transfer learning
- Python
- PyTorch
- JAX
- Distributed training frameworks
- Lifelong learning
- Meta-learning
- Online learning
- Reinforcement learning
- Memory systems
- Model adaptation
- Self-improvement
- Knowledge consolidation
Location
- San Mateo, CA
Work Type
- Onsite
Experience Level
- PhD or equivalent experience
Education Level
- PhD in Computer Science, Robotics, Engineering, Math, or a related field
Benefits
- Competitive salary
- Meaningful equity ownership
- Healthcare
- Dental
- Vision
- 401(k) match
- Unlimited PTO
About the Company
- 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 are backed in our mission by some of the top venture investors and AI luminaries.