World Models Machine Learning Expert

This is a remote, project-based role for PhD-level researchers with deep expertise in world models and generative AI. You will complete tasks at the frontier of world model research — including model development, evaluation, and research tasks spanning video prediction, environment simulation, planning, and learned latent world representations. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge AI research problems, and a strong addition to your research portfolio.

Responsibilities

• Design, build, and evaluate world models for applications spanning video prediction, environment simulation, and agent planning
• Develop and experiment with latent space representations, dynamics models, and imagination-based planning approaches
• Conduct rigorous empirical evaluations of world model architectures across diverse environments and benchmarks
• Contribute to research directions in generative modeling, self-supervised learning, and model-based reinforcement learning
• Document methodologies, experimental results, and technical approaches clearly and reproducibly

Required qualifications

• PhD in Machine Learning, Artificial Intelligence, Computer Science, or a related quantitative field (or currently enrolled and ABD)
• Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, CVPR, or equivalent)
• Demonstrated expertise in world models, generative modeling, or model-based reinforcement learning
• Strong problem-solving skills and ability to work independently on open-ended research tasks

Preferred qualifications

• Experience with video generation or prediction models (e.g., RSSM, DreamerV3, JEPA, or similar architectures)
• Familiarity with model-based RL frameworks and environments (e.g., MuJoCo, DMControl, Atari, or similar)
• Background in TA'ing or teaching deep learning, reinforcement learning, or generative modeling courses

Why apply

• Flexible Time Commitment – Work on your schedule while tackling meaningful research challenges
• Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart
• Exceptional Pay – Project-based pay ranges from $150–$200/hour
• Portfolio Building – Gain experience working on frontier world model research problems
• Professional Growth – Sharpen your skills on varied, challenging generative modeling and simulation tasks