Staff AI/ML Research Engineer

San Francisco, California
Artificial Intelligence /
Full Time /
Hybrid
> about P-1 AI

Our goal at P-1 AI is to develop an artificial general engineering (super)intelligence that can help the human species design physical systems more efficiently and at unprecedented levels of complexity. Going beyond existing foundation models, our autonomous AI agent learns from synthetic training data and real-world feedback and reasons over an internal multi-physics representation of a product design that encompasses both geometry and function. We are a cracked team of (ex-DeepMind, Microsoft, Airbus, DARPA, etc.) AI researchers, engineers, entrepreneurs, and top industry executives backed by some of the best investors in Silicon Valley and beyond.

> about the role

As a Research Engineer here, you will be responsible for building and deploying AI systems ([multimodal] LLMs, GNNs, etc.) with quantitative reasoning capability that can perform previously impossible tasks or achieve unprecedented levels of performance in the domain of designing physical systems. We're looking for people with solid engineering skills, writing bug-free machine learning code, and building the science behind the systems employed (algorithms, data, evals). You will get exposure and will be expected to solve and take ownership of components across the entire stack. You will be interfacing with simulation engineering and domain experts to deploy this technology on real-world problems.

> tech stack

Python
PyTorch
C++

> location

This is a hybrid role but principally based in San Francisco. Candidates are expected to be located in the Bay Area or open to relocation.

> we expect you to

* have strong programming skills and deep understanding of machine learning
* have experience working with large distributed systems
* be comfortable diving into a large ML codebase to debug
* have a deep understanding of LLM architectures and sophisticated understanding of model inference
* have experience with LLM post-training
* execute and analyze experiments autonomously and collaboratively
* be excited about the prospect of building AG(E)I

> ++

* keeping up with state-of-the-art LLM research
* familiarity with graph neural networks
* you’ve built an impactful/popular open-source project
* you’ve published/co-authored papers on LLMs

> you will thrive in this role if

* you have a background in statistical machine learning, physics, mathematics, or another theoretically and empirically rigorous field
* you love working in a fast-paced, dynamic startup environment
* you are intellectually curious and quick to pick up concepts outside of your direct areas of expertise
$200,000 - $200,000 a year
This role includes a significant equity component. We are an early-stage startup, so we favor equity over cash in our current compensation philosophy. You should too, or an early-stage startup might not be for you. That said, we expect cash compensation to progress quickly as the company matures.