5+ years of experience in machine learning or research, focusing on LLMs and transformer models
Strong ML fundamentals with a broad range of research interests
Proficiency in Python and systems programming, with experience in PyTorch or JAX
Demonstrated problem-solving skills and ownership of end-to-end solutions
Passion for keeping up with the evolving ML infrastructure landscape
Ability to meet throughput expectations and respond quickly to feedback
Responsibilities
Design and build reinforcement learning environments and reward functions for ML tasks
Develop deep expertise in ML research, training, and inference infrastructure
Collaborate with team members to brainstorm and create innovative tools for environment building
Conduct experiments and evaluations to refine models
Deliver work into production training runs
Benefits
Competitive cash and equity compensation
Ownership and autonomy in a fast-paced startup environment
Opportunity to collaborate with top machine learning engineers
Comprehensive health, vision, and dental benefits
401K match
Daily onsite lunch and weekly snack orders
Visa sponsorship and relocation support available
Full Job Description
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.
