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# Senior AI Researcher - World Foundation Models

- **Company:** NVIDIA Ltd.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $184,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Code Generation, Profiling, Nvidia CUDA, Computer Engineering, Software Debugging, Python (Programming Language), Node.Js, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Model Validation, Information Technology, Optimization Algorithms
- **Published:** November 2, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-ai-researcher-world-foundation-models-ca--a6b09ed8-32ba-4815-bf81-fe193db8859f

## About the Role

Research Skills, Artificial Intelligence (AI), Autonomous Driving Systems, GPU (Graphics Processing Unit), Artificial Intelligence (AI), Simulation, Product Engineering, Model Validation, Algorithms, Prototyping, * 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 similar area.
 * 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 from the crowd:

 * 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.

## Description

* 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.