Preference Model

Member of Technical Staff - Machine Learning Capabilities

San Francisco · Staff+

Sponsorship not specified$44k-$592kDetected 6 days ago

PythonMachine LearningPyTorchNLPLLMsResearch

About the role

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.
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.

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
Visa sponsorship & relocation support available

You have strong ML fundamentals and broad research interests.
Proficiency in Python and systems programming and at least one of PyTorch or JAX
Ability to meet throughput expectations and respond quickly to feedback
Have deep understanding of transformer internals, training/inference of modern LLMs, experience with inference libraries (vLLM, SGLang, etc)
Have strong expertise in kernel development (CUDA, Triton, Pallas)
5+ years of experience working in machine learning or research, primarily on LLMs and transformer models
You have 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
Problem solvers who take ownership and drives solutions end-to-end
Passion for staying current with the rapidly evolving ML infrastructure landscape
Have 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
Have built complex interactive RL environments
Competitive cash and equity compensation (>90th percentile)
Ownership and autonomy in a fast moving startup environment
Opportunity to work with top machine learning engineers
Health, vision, dental, benefits

Have 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

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.
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.

This listing is sourced directly from Preference Model's careers page and normalized into a canonical job model.