Member of Technical Staff - Research Engineer, RL Environments

Preference Model, Inc.; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Competitive cash and equity

Ownership and autonomy

Work with top ML engineers

Health, vision, dental, benefits

401K match

Lunch onsite

Weekly snack orders

Visa sponsorship & relocation support

Job summary

Preference Model, Inc. is hiring experienced Machine Learning Engineers to design and build reinforcement learning environments for frontier models. You will teach frontier models to perform the work of ML engineers within a frontier lab.

This role blends research and engineering, requiring you to stay up to date with the latest research, develop novel approaches, and realize them in code. You will have full ownership of the environments you build and deploy them into production training runs,

Qualifications

• 5+ years of experience in ML or research focused on LLMs and transformer models.
• Strong ML fundamentals and ability to translate research into RLVR problems.
• Proficiency in Python and systems programming; knowledge of PyTorch or JAX.
• Ownership mindset and end-to-end delivery capability.
• Keeps up with evolving ML infrastructure.

Responsibilities

• Design and build RL environments and reward functions for frontier models on ML research and engineering tasks.
• Develop deep expertise across ML research, training, and inference infrastructure.
• Collaborate with others to brainstorm and create new ideas and tools to improve the environment building process.

Skills

Python

Systems programming

LLMs / Transformer models

Problem solving

Throughput awareness

Tools

PyTorch

JAX

Job description

About Us

Preference Model is a superintelligence data research company. We build RL environments for training capable, better-aligned superintelligences.

Almost every aspect of how a model behaves is shaped by the reward signals it's trained on. The biggest problem with AIs today is that they don't always do what we intend. Sometimes that's because they aren't capable enough, and sometimes it's because they aren't aligned. Both problems come down to the training objectives.

We're trying to address both of these issues in the most direct, highest leverage way available to us, which today means researching how to build RL environments better, not just for the AIs of today but for the AIs as they will be when they outsmart us.

Over the past year, we've built RL environments for several frontier labs, and we're backed by $16M in seed funding led by a16z. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude.

About the Role

We’re hiring experienced 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 only for experienced ML Engineers. We have a separate opening for New Grads.

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):

• 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

• 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 you also:

• 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 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)

• Have built complex interactive RL environments

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.