Senior Deep Learning Engineer - Model Evaluation & AI Systems

About NVIDIA

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

About the Role

This is an outstanding opportunity to join NVIDIA, a company at the forefront of AI and high-performance computing. As a Senior / Principal Deep Learning Engineer — Model Evaluation & AI Systems, you will play a meaningful role in crafting the future of AI. Your work will have a direct impact on our product releases and positioning in the market.

What you'll be doing:

• Define and build evaluation methodologies for innovative AI models, including LLMs, RAG systems, agents, and vision/multimodal models.
• Build and expand NeMo Evaluator as an open-source platform, focusing on correctness, reproducibility, and ease of adoption.
• Build scalable, reproducible evaluation infrastructure, including harnesses, orchestration, and result pipelines running on large GPU clusters.
• Collaborate with and engage the open-source community, reviewing contributions, shaping the roadmap, and sharing best practices.
• Work alongside model training, inference, and product divisions to provide trusted evaluation signals that inform release and optimization decisions.

What we need to see:

• BS, MS, or PhD in Computer Science, AI, Applied Math, or a related field, or equivalent experience.
• Senior-level experience (typically 12+ years) developing or assessing contemporary machine learning and deep learning systems.
• Hands-on experience with large language models and NLP, including model behavior analysis and evaluation.
• Demonstrated experience contributing to open-source software or building platforms, libraries, or tools used by other engineers.
• Ability to take charge of unclear technical challenges and communicate effectively across research, engineering, and product teams.

Ways to stand out from the crowd:

• Experience building or improving evaluation frameworks, benchmarks, or ML infrastructure used by other teams or external users.
• A strong appreciation for evaluation quality, including correctness, reproducibility, and consistency across environments.
• Hands-on experience evaluating modern AI systems such as LLMs, RAG pipelines, agents, or multimodal models.
• Prior involvement in open-source projects, through contributions, reviews, maintenance, or community engagement.
• Experience acting as a technical bridge across teams or platforms (e.g., evaluation, training, or agent frameworks), combining architectural understanding with clear communication and influence.