Description
We're building the next generation of AI systems that can perceive, reason about, and generate dynamic worlds. Our team advances world foundation models to enable high-fidelity, temporally stable video and world generation for Physical AI, simulation, and interactive experiences.
As a Senior AI Engineer, you'll develop and validate model architecture and algorithm changes that improve video generation fidelity, with emphasis on human-centric quality. You'll explore and prototype improvements across spatial multimodal modeling, modality alignment, flow-based or diffusion-based video generation, and neural rendering-inspired representations to improve controllability and long-horizon consistency.
You'll improve training and inference efficiency through architectural and post-training techniques (compute/memory optimizations, distillation, pruning, and compression). You'll define model training objectives that improve sim-to-real and real-to-sim generalization, especially for human motion, contact, and interaction dynamics across real-world and synthetic/simulation data.
You'll develop detailed, domain-specific benchmarks for evaluating world foundation models, especially generation and understanding world models that reason about video, simulation, and physical environments. You'll translate research results into robust implementations like training code, production-grade checkpoints, model integrations, and demos that clearly showcase capability gains across teams.
We're looking for a PhD in Computer Science, Graphics, Computer Engineering, or a closely related field (or equivalent experience). You should have 8+ years of applied research and/or industry experience in vision, graphics, or adjacent ML domains or similar area. You'll need 3+ years of direct experience designing, training, and evaluating generative models for image/video/audio, with strong fundamentals in modern deep learning.