Member of Technical Staff - Open Endedness

About V_max

V_max is an applied research lab working at the frontier of reinforcement learning (RL). We are building new techniques for leveraging RL with Large Language Models (LLMs). Our research contributes directly to our RL platform, which automates the engineering involved in converting data and evals into RL environments.

About the role

Our goal is to automate the design of tasks for RL agents to help them learn domain specific skills. In this role you will develop approaches to optimize the construction of tasks to maximize resulting agent performance.

Responsibilities

• Develop new approaches to task construction - building on literature in open endedness and unsupervised environment design
• Develop new reward functions for environment design
• Benchmark agents that learn in generated environments
• Validate your research on industry specific problems

Role Requirements

• AI PhD or equivalent experience
• Track record of research excellence, as demonstrated by publications, open source work or publicly deployed AI systems
• Deep understanding of RL and ML
• Expertise with Python and an ML framework (PyTorch, JAX)

Nice to have

• Experience in post-training LLMs
• Experience researching evolutionary optimization
• Experience researching unsupervised environment design
• Skilled in presenting the results and implications of your work to multiple levels of audience

Role specific location policy

• this role is based in our San Francisco office; for exceptional candidates we are willing to consider a hybrid arrangement

Compensation

The expected salary range for this position is $250,000 - $450,000 USD