Member of Technical Staff - Machine Learning Capabilities

Preference Model•San Francisco, CA

13d•$200,000 - $350,000•Onsite

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. We’re hiring experienced Machine Learning Engineers to design and build reinforcement learning environments to safely advance model capabilities in machine learning research and engineering. Specifically, you'll be teaching frontier models to do the work of an ML engineer or researcher at a frontier lab. This role blends research and engineering. It will require you to stay up to date with the latest research, develop novel approaches, and realize them in code. You will have full ownership and autonomy of the environments you build. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers and engineers. You will join our Capabilities org, a small, high-ownership team and contribute directly to the data layer that powers frontier LLM capability. Note: This role is only for experienced ML Engineers. We have a separate opening for New Grads.

Requirements

5+ years of experience working in machine learning or research, primarily on LLMs and transformer models
Strong ML fundamentals and broad research interests. You read many papers or tutorials, understand topics deeply and have the creativity to translate them into RLVR problems
Proficiency in Python and systems programming and at least one of PyTorch or JAX
Problem solvers who take ownership and drives 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 DL/ML research area, with publications or public code to show for it. Research experience (PhD, MS) is a big plus
Deep understanding of transformer internals, training/inference of modern LLMs, experience with inference libraries (vLLM, SGLang, etc)
Strong expertise in kernel development (CUDA, Triton, Pallas)
Built complex interactive RL environments

Responsibilities

Design and build RL environments and reward functions that produce clean, learnable signals for frontier models on ML research and engineering tasks
Build deep expertise across the frontier of ML research, training, and inference infrastructure
Collaborate with others to brainstorm and create new ideas and tools to improve the environment building process