AI/ML Research Engineer - LLM Post-Training with RL

P-1 AI; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A leading company in AI is seeking a Research Engineer to develop advanced AI systems capable of quantitative reasoning. The role involves training LLMs and requires strong programming and machine learning skills. Ideal candidates will thrive in a dynamic startup environment and have a rigorous academic background.

Qualifications

• Strong programming skills and deep understanding of machine learning.
• Experience with LLM post-training focused on RL.

Responsibilities

• Responsible for RLEF training of LLM/agentic systems.
• Build AI systems with quantitative reasoning capabilities.

Skills

Programming

Machine Learning

Debugging

Quantitative Reasoning

Education

Background in Statistical Machine Learning

Background in Physics

Background in Mathematics

Tools

Python

PyTorch

C++

Job description

> 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

As a Research Engineer here, you will be responsible for RLEF training (Reinforcement Learning from Execution Feedback) of our LLM/agentic systems and helping us build an AI system with quantitative reasoning capability that can perform previously impossible tasks or achieve unprecedented levels of performance in the domain of designing physical systems.

> tech stack

• Python
• PyTorch
• C++

> we expect you to

• have strong programming skills and a deep understanding of machine learning
• 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 with a focus on RL
• execute and analyze experiments autonomously and collaboratively
• be excited about the prospect of building an engineering AGI

> ++

• you’ve built a popular/impactful open source project
• you’ve published or co-authored LLM/RL papers
• keep up with state-of-the-art LLM research

> you will thrive in this role if

• you have a background in statistical machine learning, physics, mathematics, or another theoretically rigorous field
• you are intellectually curious and quick to pick up concepts outside your area of expertise
• you love working in a dynamic, fast-paced startup environment

> interview process

• Initial screening - with Head of Talent (30 mins)
• Hiring manager interview - with co-founder & Head of AI (30 mins)
• Technical interview 1 (60 mins)
• Technical interview 2 (60 mins)
• Culture fit / Q&A (possibly in-person) - with co-founder & CEO