Staff Research Engineer - Post Training

> about P-1 AI

Building Engineering AGI for the physical world

> about the role

As a Research Engineer here, you will be responsible for building and deploying AI systems ([multimodal] LLMs) 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++

> 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

• 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

• 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

> 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.

> interview process

• Recruiter screen with Head of Talent (30 mins)

• Hiring manager interview with Head of AI (30 mins)

• Technical Interview 1 - SWE skills (1 hour)

• Technical Interview 2 - ML debugging & research literature discussion (1 hour)

• CEO interview (45 mins)
  

Please note that the technical interviews may be done in a different order.