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:
As a Machine Learning Research Engineer you will be creating the most critical AI features of the core Archie product. You will work closely with fellow AI research scientists, forward deployed engineers, software engineers and subject matter experts to build cutting edge AI capabilities that can perform real engineering design tasks.
In this role, you are expected to take ownership and do whatever it takes to deliver game changing capabilities. Your key technical decisions will directly impact how the world’s largest manufacturers and engineers throughout the world design and build physical products. You’ll have the chance to build and deliver applications that leverage state of the art tools in Machine Learning and AI to shape the physical world around us.
What You’ll Do:
Learn from leading experts in aerospace, electrical, mechanical and automotive engineering to develop AI tools and features that solve real design engineering problems.
Collaborate with research scientists to help train large language models and transition them into a core product (Mid-Training, SFT, RL, Post-Training).
Build new agentic features and integrations with major engineering design tools.
Who You Are:
Experience transitioning AI research prototypes into delivered products.
Experience building physical systems (Aerospace, Mechanical, Robotics, other).
Deep learning experience with strong fundamental understanding about machine learning.
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.
Compensation:
Salary: $200k - $265k.
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.
Interview process:
Initial screening (30 mins)
Biographic/Behavioral interview (45 mins)
Technical Interview (60 mins)
Culture fit / Q&A with CEO (30 mins)
