About the role

Senior AI Engineer at NVIDIA focused on developing generative models for high-fidelity video generation. Working on advanced AI systems that enhance real-world usability and visual quality.

Responsibilities

Research, implement, and validate model architecture and algorithm changes that improve video generation fidelity, with emphasis on human-centric quality (identity preservation, anatomy, motion coherence, and interaction realism)
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
Improve training and inference efficiency through architectural and post-training techniques (compute/memory optimizations, distillation, pruning, and compression)
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
Develop detailed, domain-specific benchmarks for evaluating world foundation models, especially generation and understanding world models that reason about video, simulation, and physical environments
Translate research results into robust implementations like training code, production-grade checkpoints, model integrations, and demos that clearly showcase capability gains across teams

PhD in Computer Science, Graphics, Computer Engineering, or a closely related field (or equivalent experience)
8+ years of applied research and/or industry experience in vision, graphics, or adjacent ML domains (or equivalent experience)
4+ years of direct experience designing, training, and evaluating generative models for image/video/audio, with strong fundamentals in modern deep learning
Hands-on experience improving generative models with a focus on perceptual quality and temporal stability, especially for generating humans
Advanced proficiency in Python, PyTorch, C++, and CUDA with strong research-engineering practices (reproducibility, testing, profiling, experiment tracking)
Experience training and debugging large models in multi-GPU and/or multi-node environments and distributed training workflows
Practical knowledge of inference/runtime bottlenecks and optimization techniques (e.g., batching, parallelism strategies, low-precision/quantization awareness, attention/KV-cache efficiency)
Strong “eye for quality” and interest in diagnosing visual artifacts (sharpness, texture detail, temporal stability, etc.) using perceptual metrics, human preference signals, or learned evaluators.