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—helping mankind conquer nature and bend it to our will. Our first product is Archie, an AI engineer capable of quantitative and spatial reasoning over physical product domains that performs at the level of an entry-level design engineer. 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 just 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 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.
What You’ll Do:
Design and implement agentic systems that autonomously solve physical engineering tasks.
Develop advanced memory, retrieval, and planning architectures for LLM-based agents.
Apply (or invent) reinforcement learning strategies for reasoning, planning, and online adaptation.
Contribute to both research strategy and technical implementation—this is a hands-on role.
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.
About you:
Have experience at the frontier of AI research (e.g., LLMs, RL, memory systems, agent architectures).
Are passionate about applied problems—especially in mechanical, aerospace, or electrical engineering domains.
Are fluent in Python and major ML/AI frameworks (PyTorch, JAX, etc.).
Thrive in fast-moving environments and feel comfortable working towards underspecified goals.
Have a “whatever it takes” mindset: you’re the kind of person who makes things work.
Can go from whiteboard to working prototype without waiting for someone else to “engineer” it.
Preferred Qualifications:
PhD in Computer Science, Robotics, Engineering, Math, or a related technical field (or equivalent experience).
Relevant publications in top-tier venues of your field.
Experience with physical engineering domains a plus.
Familiarity with agent tool-use, retrieval-augmented generation, or long-term memory systems.
Deep knowledge of reinforcement learning algorithms and practical challenges.
Interview process:
Initial screening - Head of Talent (30 mins)
Hiring manager interview - Head of AI (45 mins)
Technical Interview - AI Chief Scientist (45 mins)
Culture fit / Q&A (maybe in person) - with co-founder & CEO (45 mins)
Top Skills
What We Do
Building engineering AGI for the physical world.
.png)
