Relational Foundation Model Engineer — Graph & Transformer AI

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

Job summary

NVIDIA Corporation in Santa Clara invites a senior ML research engineer to design, build, and evaluate Transformer and graph neural network architectures that operate over relational schemas and heterogeneous graphs. You will push models from research to production, powering enterprise AI across recommendations, forecasting, fraud detection, and predictive maintenance.

You will collaborate with researchers and engineers across the ML lifecycle, from architecture exploration to large-scale

Qualifications

• MS or PhD in Machine Learning, CS, or equivalent experience.
• Proficiency in Python and PyTorch.
• At least 8 years of research experience in ML design.
• Experience applying predictive models in real-world applications.

Responsibilities

• Collaborate with researchers/engineers to enhance Transformer and GNN models for relational schemas and heterogeneous graphs.
• Gain hands-on experience with high-impact use cases such as forecasting, entity matching, customer retention and fraud detection.
• Leverage ML knowledge to tackle real challenges and develop scalable, adaptable solutions.
• Work across the full ML lifecycle from architecture design/training to post-training optimization and inference acceleration.
• Contribute to the next generation of the Relational Foundation Model.

Skills

Python

PyTorch

ML research

Education

MS or PhD in Machine Learning, CS, or equivalent experience

Job description

NVIDIA Corporation in Santa Clara invites a senior ML research engineer to design, build, and evaluate Transformer and graph neural network architectures that operate over relational schemas and heterogeneous graphs. You will push models from research to production, powering enterprise AI across recommendations, forecasting, fraud detection, and predictive maintenance.

You will collaborate with researchers and engineers across the ML lifecycle, from architecture exploration to large-scale