Medal is looking to develop the next generation of intelligent agents and simulation systems that can understand, predict, and act in complex 3D environments by designing, training, and evaluating world models and action policies.
Requirements
- 5+ years of experience in deep learning research or reinforcement learning, with a focus on embodied agents or simulation environments.
- Strong foundation in representation learning and generative modeling, particularly using architectures such as diffusion models, VAEs, and transformers applied to video.
- Experience with world models and predictive control
- Proficiency in reinforcement learning (RL, model-based RL, or imitation learning)
- Programming fluency in Python and deep learning frameworks such as PyTorch.
- Strong experimental skills — comfort with large-scale training, evaluation pipelines, and managing complex datasets or simulations.
- Experience with Unity, Unreal Engine, or custom simulators is a plus.
Responsibilities
- Designing, training, and evaluating world models and action policies that operate within games and based on gaming data.
- Experimenting rapidly, iterating on architectures, and collaborating closely with our product and engineering teams to bring research ideas to production.
- Training models that simulate dynamics and plan actions in learned environments.
- Designing and evaluating policy networks.
- Managing complex datasets or simulations.
- Translating research into production systems, with an understanding of compute efficiency, distributed training, and data bottlenecks.
- Running continuous evaluation, A/B testing, and performance metrics tracking on our deployed models.
Other
- In-person: Looking to hire in NYC. 5 days in the office.
- Ownership & scientific rigor: You see ideas through from concept to proof to deployment.
- Curiosity-driven and result-oriented: You’re excited by open-ended problems, but you also know how to define measurable goals and ship impactful systems.
- Gaming & simulation passion: Interest in interactive environments, physics-based simulations, or gaming AI.
- Collaborate closely with our product and engineering teams
