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 new grad ML Engineers to design and build reinforcement learning environments to safely advance model capabilities specifically on machine learning research and engineering tasks to do the work of an MLE at a frontier lab.

This role blends research and engineering. It will require you to both develop novel approaches and realize them in code. 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 ML Capabilities org, 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 schemes 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; ideally PyTorch or JAX

• Smart 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 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.

• Deep understanding of transformer internals

• Strong expertise in kernel development (CUDA, Triton, Pallas), optimizing non-trivial neural modules to specific hardware

• Research projects, coursework, or personal work involving RL environments (any framework, any scale)

• Open-source contributions to ML infrastructure or RL tooling

• Experience with any cloud platform (AWS, GCP, Azure) or infrastructure-as-code tools

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

• Lunch provided everyday onsite

• Weekly snack orders

• 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.

Note: We utilize AI note-taking during our interview sessions to ensure we capture all answers and details accurately. Candidates are allowed to use AI note-takers as well, however, no other AI tools are permitted during any live interviews.

Compensation (from employer):
SF Onsite: Estimated base salary range for candidates onsite at our San Francisco office $165K – $200K • Offers Equity • Offers Bonus • Bonus based on performance