Relational Foundation Model Engineer — Graph AI (Equity)

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

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking an accomplished ML researcher to join the Relational Foundation Model team in California. You will design, build, and evaluate Transformer and graph neural network architectures that generalize across relational data and heterogeneous graphs, powering enterprise AI applications.

Collaborate with researchers and engineers across the full ML lifecycle, tackle real-world forecasting, fraud detection and entity matching, and contribute to scalable, production-grade systems built on

Qualifications

• MS or PhD in Machine Learning, Computer Science, or equivalent experience.
• Proficiency in Python and PyTorch.
• At least 8 years of research experience in designing ML algorithm solutions.
• Practical experience applying predictive models to real-world applications.

Responsibilities

• Collaborate with researchers/engineers to enhance Transformer and GNN-based models over relational schemas and graphs.
• Work on high-impact use cases such as forecasting, entity matching, customer retention and fraud detection.
• Contribute to scalable and adaptable ML solutions and inference acceleration.
• Participate across the ML lifecycle from design and training to optimization and deployment.
• Contribute to the next generation of the Relational Foundation Model.

Skills

Python

PyTorch

ML algorithm design

Education

MS or PhD in Machine Learning, Computer Science, or equivalent experience

Job description

NVIDIA is seeking an accomplished ML researcher to join the Relational Foundation Model team in California. You will design, build, and evaluate Transformer and graph neural network architectures that generalize across relational data and heterogeneous graphs, powering enterprise AI applications.

Collaborate with researchers and engineers across the full ML lifecycle, tackle real-world forecasting, fraud detection and entity matching, and contribute to scalable, production-grade systems built on