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 hiring experienced ML Engineers to design and build reinforcement learning environments to teach LLMs better reasoning / advanced concepts from modern ML with the goal to automate machine learning.
You'll join a small, high-ownership team and contribute directly to the data layer that powers frontier LLM capability.
What You Will Do:
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.
What We are Looking For (Qualifications):
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.
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
You may be a good fit if one of the following applies
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)
What We Offer:
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
401K match
Visa sponsorship & relocation support available
We value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.
