Member of Technical Staff - Research Engineer, RL Environments

Preference Model•San Francisco, CA

About The Position

Preference Model is hiring an experienced Machine Learning Engineer to design and build reinforcement learning environments that safely advance model capabilities in machine learning research and engineering. The role focuses on teaching frontier models to perform the work of ML engineers and researchers at frontier labs. This position combines research and engineering: the successful candidate will stay current with the latest research, develop novel approaches, and implement them in code. The role offers full ownership and autonomy over the environments built. Work includes designing and implementing RL environments, conducting experiments and evaluations, delivering work into production training runs, and collaborating with researchers and engineers. The hire will join the small, high-ownership Capabilities organization and contribute to the data layer powering frontier LLM capabilities. This opening is for experienced ML engineers; Preference Model has a separate opening for new graduates.

Requirements

At least 5 years of experience working in machine learning or research, primarily on LLMs and transformer models.
Strong machine learning fundamentals and broad research interests.
Ability to read papers or tutorials, understand topics deeply, and translate them creatively into RLVR problems.
Proficiency in Python and systems programming, and proficiency in at least one of PyTorch or JAX.
Ability to take ownership, solve problems, and drive solutions end to end.
Passion for staying current with the rapidly evolving ML infrastructure landscape.
Ability to meet throughput expectations and respond quickly to feedback.

Nice To Haves

Expert knowledge in an active deep learning or machine learning research area, with publications or public code to demonstrate it.
Research experience; a PhD or MS is a plus.
Deep understanding of transformer internals and training or inference of modern LLMs, with experience using inference libraries such as vLLM or SGLang.
Strong expertise in kernel development, including CUDA, Triton, or Pallas.
Experience building complex interactive RL environments.

Responsibilities

Design and build RL environments and reward functions that produce clean, learnable signals for frontier models working on ML research and engineering tasks.
Build deep expertise across frontier ML research, training, and inference infrastructure.
Collaborate with others to develop ideas and tools that improve the environment-building process.
Conduct experiments and evaluations.
Deliver work into production training runs.