Member of Technical Staff - Machine Learning Capabilities, New Graduates

The role

New graduate Machine Learning Engineers will design and build reinforcement learning environments to advance model capabilities. The role blends research and engineering to teach frontier models ML research and engineering tasks.

What you will do

• Design and build RL environments and reward schemes.
• Build deep expertise across the frontier of ML research, training, and inference infrastructure.
• Collaborate to brainstorm and create new ideas and tools to improve the environment building process.
• Conduct experiments and evaluations.
• Deliver work into production training runs.

Required qualifications

• Strong ML fundamentals and broad research interests.
• Expert knowledge in an active DL/ML research area, with publications or public code.
• Deep understanding of transformer internals.
• Proficiency in Python, Numpy, and systems programming.
• 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

• Research experience (PhD)
• 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

Benefits

• 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

Before you apply

• This role is for recent graduates only who can start soon.

About the company

Preference Model is a superintelligence data research company that builds RL environments for training capable, better-aligned superintelligences. The founding team has previous experience on Anthropic’s data team.