Location: San Francisco preferred, remote considered

Compensation: Paid internship

About Us

Preference Model is building automated ML research engineering.

Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.

Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.

About the Role

We're looking for PhD or Master's students, and gifted undergrads to spend an internship with us working on building RL training environments for large language models.

What you'll do

• Design and build RL environments that test LLM reasoning on ML, systems, and research problems

• Write clean, production-grade Python (not notebooks)

• Work with Docker, build reproducible environments, debug when things break

• Translate ML papers and concepts into concrete training tasks

What We are Looking For (Qualifications):

You're an undergrad or PhD student in CS, ML, math, physics, or a related field. You write real code, not just research prototypes. You read ML papers for fun in your free time.

Must have

• Strong Python skills

• Familiarity with how LLMs work, what they're good at, and where they fall short

• Ability to work independently, take feedback, and iterate fast

You may be a good fit if one of the following applies

• You understand transformer internals and have worked with training or inference code

• You've written CUDA kernels or worked with low-level GPU programming

• You have a research area you know deeply (publications, public code, or strong coursework)

• You read broadly across ML and can connect ideas from different subfields

• You've built interactive environments, simulations, or complex software systems

What We Offer:

• Paid Internship with opportunity to return full time based on performance

• Ownership and autonomy in a fast moving startup environment

• Opportunity to work with top machine learning engineers