Machine Learning Engineer, RL Environments - Internship

Preference Model•San Francisco, CA

2d•Hybrid

About The Position

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.

Requirements

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.
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

Nice To Haves

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

Responsibilities

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