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Vmax

Automates generation of reinforcement learning environments

Member of Technical Staff - Open Endedness

AI ResearchConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$300k - $500k

About the Job

Requirements

  • A PhD or equivalent experience in machine learning, reinforcement learning, artificial intelligence, or a closely related field.
  • A 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 large language model 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 machine learning 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.

Responsibilities

  • Develop reinforcement learning 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 or 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, from identifying promising directions to executing experiments and communicating results.

Desired Qualifications

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

About the Company

Vmax

Automates generation of reinforcement learning environments

Vmax.ai builds tools to automate reinforcement learning (RL) development. It creates scalable RL environments from proprietary data so engineers can train agents for long-horizon tasks without a lot of manual RL engineering. The core product concept is to automatically transform company data and evaluation metrics into reusable RL environments, enabling post-training of large language model–based agents for domain-specific use cases. The company distinguishes itself by combining automated environment design with data-driven RL environment generation, aiming to cut human intervention in RL workflows. The founding team’s deep RL background, stealth mode status, and backing from South Park Commons position it to pursue enterprise-grade RL automation and domain-specific AI through a platform that handles data-to-environment conversion and subsequent agent fine-tuning. Its goal is to scale RL development by reducing setup work and enabling long-horizon tasks across domains.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
San Francisco, CA
Founded
2025

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