This role includes a significant equity component. We are an early-stage startup, so we favor equity over cash in our current compensation philosophy. This role is best suited for candidates who value long-term ownership and impact over short-term cash optimization.

Our benefits include healthcare, dental, and vision insurance, 401k with employer matching, unlimited PTO, and meaningful equity.

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.

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.

What You’ll Do:

• 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).

Who You Are:

You likely:

• 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.

As a bonus, you may have:

• 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

Our Values:

Mission obsession & urgency: We are obsessed with building engineering AGI as quickly as possible. We also recognize that as a startup, speed is our most precious competitive advantage. We are constantly asking ourselves what we can do to go faster. We make tradeoffs and sacrifices (personally and in the workplace) in exchange for speed.

Intellectual excellence & curiosity: We ask “what if?” and experiment liberally. We always look for better ways of doing something. We read voraciously. We challenge each other to be better. We surround ourselves with A players and we actively and unapologetically reject B players (and even B+ players - because they tend to surround themselves with C players).

Shipping discipline: We treat production with respect. We test and demo our product constantly. We listen attentively to our customers, users, and stakeholders, and we respect our commitments to them. We also respect our commitments to each other and will go the extra mile (or ten or one hundred) to honor them.

Ownership: We all have significant ownership stakes in the company and operate in founder mode. We believe in hierarchical requirements but not in hierarchical information flows. If we see that something is broken or can be done better, we flag it and we fix it. We encourage each other to play with and fix anything and everything... but there’s a clear owner for everything.

Interview process:

• Initial screening - Head of Talent (30 mins)

• Biographic/Behavioural interview - Head of AI (45 mins)

• Technical Interview - Member of Technical Staff (60 mins)

• Culture Fit / Q&A (maybe in person) - with co-founder & CEO (30 mins)

Compensation Range: $250K - $350K