Conversant, ideally deep, in several of: SSL for visual and sensor representations; world models (JEPA, V-JEPA, I-JEPA, LeJEPA, MJEPA); generative and predictive architectures (diffusion, DiT, flow matching, VAEs); robotics ML (VLA, inverse dynamics, sim-to-real, optical flow); sensor fusion (vision, proprioception, force/torque, multi-modal encoders); PyTorch, JAX, and distributed training. Advance our self-supervised learning stack for visual and sensor representations, building on and extending the JEPA family (V-JEPA, I-JEPA, and related predictive-embedding approaches).