Senior ML Engineer - Cosmos AI & Foundation Models

NVIDIA Corporation; Santa Clara (CA); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Job summary

NVIDIA Corporation in Santa Clara, CA, is seeking a senior ML researcher to build Metropolis AI solutions atop Cosmos foundation models for intelligent video analytics and perception.

You will lead end-to-end projects, collaborate with product, engineering, and data procurement teams, and contribute to open-source artifacts. Requires 8+ years in ML, MSc/PhD, diffusion models, LLMs/VLMs, and large-scale foundation models.

Qualifications

• MSc/PhD in CS, EE or related field or equivalent experience.
• 8+ years of proven experience in applied machine learning or AI research.
• Deep expertise in deep learning fundamentals, with hands-on diffusion models and generative architectures.
• Experience in pre-training or refining LLMs, VLMs, or WFMs.
• Experience with large-scale foundation models, training workflows, fine-tuning techniques, and evaluation approaches.

Responsibilities

• Build and deliver Metropolis AI solutions powered by Cosmos models for intelligent video analytics and perception.
• Identify model underperformance and propose improvements including synthetic data generation and architecture refinements.
• Collaborate with Cosmos team to shape platform features aligned with product roadmap.

Skills

Diffusion models

Generative architectures

LLMs

VLMs

World foundation models

End-to-end project leadership

Education

MSc or PhD in Computer Science, Electrical Engineering, or related field

Tools

CUDA

Triton

Simulation environments (e.g., Isaac Sim)

Job description

NVIDIA Corporation in Santa Clara, CA, is seeking a senior ML researcher to build Metropolis AI solutions atop Cosmos foundation models for intelligent video analytics and perception.

You will lead end-to-end projects, collaborate with product, engineering, and data procurement teams, and contribute to open-source artifacts. Requires 8+ years in ML, MSc/PhD, diffusion models, LLMs/VLMs, and large-scale foundation models.