World Model / Action Policy Researcher
Core
Researching foundation models for environments requiring deep spatial and temporal reasoning, specifically world models and action policies for embodied agents.
Role type
Senior IC research scientist (world models & action policies)
Builds
World models that provide training environments for agents capable of navigating space and time
Domain
AI research, embodied AI, simulation environments
Deliverable
production ML models
Required skills
deep learning research, reinforcement learning, representation learning, generative modeling, diffusion models, VAEs, transformers for video, predictive control, policy network design, Python, PyTorch, large-scale training, experimental design
Preferred skills
publications in world modeling/simulation learning, Unity/Unreal Engine experience, gaming AI passion
Technologies
Python, PyTorch, NumPy, Triton, CUDA, Kubernetes, GCP
Responsibilities
Design and evaluate policy networks, train models that simulate dynamics and plan actions in learned environments, manage complex datasets or simulations, write clean reproducible code, define measurable goals for open-ended problems
Seniority
Senior, hands-on IC
