Senior AI Researcher - World Foundation Models

NVIDIA Gruppe; California (MO); On-site; USD 184,000 - 356,500; Full time

Job summary

NVIDIA Gruppe is seeking a seasoned AI Researcher to develop advanced models for video generation and physical AI. This role demands both technical expertise and a PhD in a relevant field, with at least 8 years of experience in vision or graphics.

The ideal candidate will have hands-on experience with generative models and deep learning fundamentals, and will work on enhancing model fidelity and performance. A competitive salary and comprehensive benefits are offered.

Qualifications

• PhD in Computer Science, Graphics, Computer Engineering, or a closely related field.
• 8+ years of applied research and/or industry experience in vision, graphics, or adjacent ML domains.
• 3+ years of experience designing, training, and evaluating generative models for image/video/audio.
• Advanced proficiency in Python, PyTorch, C++, and CUDA.
• Experience training and debugging large models in multi-GPU and/or multi-node environments.

Responsibilities

• Research and validate model architecture for improved video generation fidelity.
• Prototype improvements in video generation and neural rendering representations.
• Define model training objectives for generalization in human motion dynamics.
• Translate research results into robust implementations and demos.

Job description

Job Overview

NVIDIAis building the next generation of AI systems that can perceive, reason about, and generate dynamic worlds. This role advances world foundation models to enable high‑fidelity, temporally stable video and world generation for Physical AI, simulation, and interactive experiences. The technical focus is on human appearance, motion and action understanding, operating at the applied‑research boundary.

Responsibilities

• Research, implement, and validate model architecture and algorithm changes that improve video generation fidelity, with emphasis on human‑centric quality.
• 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.

Qualifications

• 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.
• 3+ 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.
• 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.

Ways to stand out

• Proven track record in related research, including publications in top conferences (e.g., NeurIPS, CVPR, ICLR), with clear evidence of impact on model quality or robustness.
• Experience using agentic workflows, and AI coding companions, to accelerate research and production development, including code generation, debugging, test creation, experiment automation, benchmark development, documentation, and large‑codebase navigation.

Benefits and Compensation

Base salary: $184,000–$287,500 for Level4, and $224,000–$356,500 for Level5. Eligible for equity and benefits.

Equal Opportunity Employer

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.