Preference Model · San Francisco
Data & AIEngineeringFullTimeposted 4mo ago
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 new graduate 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 for recent graduates only who can start soon.
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.
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.
Expert knowledge in an active DL/ML research area, with publications or public code to show for it.
Research experience (PhD, MS) is a strongly preferred.
Deep understanding of transformer internals
Proficiency in Python, Numpy, 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
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
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.