Member of Technical Staff - Open Endedness

• Develop RL methods for agents that can discover useful objectives, tasks and curricula without relying entirely on human-specified rewards.
• Design systems for open-ended learning, including unsupervised/automated environment design, asymmetric self-play, and intrinsic motivation.
• Build training loops where agents learn from interaction, exploration, novelty, competence progress, self-generated challenges, or other nonstandard reward signals.
• Investigate how agents can avoid collapse into trivial, degenerate, or easily exploitable objectives.
• Own and develop a research agenda within Vmax, from identifying promising directions to executing experiments and communicating results.

• PhD or equivalent experience in machine learning, reinforcement learning, artificial intelligence, or a closely related field.
• Track record of strong technical work, demonstrated through publications, open-source projects, deployed systems, competitions, or equivalent contributions.
• Deep understanding of reinforcement learning
• Strong interest in open-ended learning
• Experience with LLM post-training
• Strong empirical research ability, including designing experiments, choosing meaningful baselines, running ablations, and diagnosing unexpected results.
• Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX.
• Ability to work independently on ambiguous research problems and turn high-level ideas into concrete experimental programs.
• Ability to collaborate effectively with researchers and engineers on ambiguous, fast-moving technical problems.
• Clear written and verbal communication of technical ideas, results, tradeoffs, and risks.

• Experience with open-ended learning, automatic curriculum generation, intrinsic motivation, self-play, goal-conditioned RL, unsupervised skill discovery, multi-agent RL, quality-diversity, or evolutionary methods.
• Familiarity with methods such as POET,  population-based training, or multi-agent RL
• Experience designing benchmarks or evals for generalization, exploration, long-horizon learning or behavioral diversity
• Demonstrated taste for identifying non-obvious research directions and converting them into tractable experiments.

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

The expected salary range for this position is $300,000 - $500,000 USD

Vmax is an applied research lab developing AI capable of open-ended learning. We are building systems to exceed humans in all capacities by optimising beyond the local maxima of learning from human expertise.