Relational Foundation Model Engineer - Graph ML + Equity

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

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is building a unified Relational Foundation Model to understand relational databases and heterogeneous graphs, enabling enterprise AI. As a researcher/engineer on this team, you will design, build, and evaluate Transformer and graph neural network architectures that generalize across diverse data schemas, powering production-ready systems for enterprise insights.

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

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 in using Predictive Models in Real World Applications

Responsibilities

• Collaborate with researchers/engineers to enhance Transformer and GNN models over relational schemas and heterogeneous graphs
• Work on high-impact use cases such as forecasting, entity matching, customer retention and fraud detection
• Contribute to scalable and adaptable ML solutions across full lifecycle from design/training to inference acceleration
• Advance production systems that translate research into deployable enterprise AI solutions
• Contribute to the next generation of the Relational Foundation Model

Skills

Python

PyTorch

Research experience

Predictive Modeling

Education

MS or PhD in Machine Learning/CS

Job description

NVIDIA is building a unified Relational Foundation Model to understand relational databases and heterogeneous graphs, enabling enterprise AI. As a researcher/engineer on this team, you will design, build, and evaluate Transformer and graph neural network architectures that generalize across diverse data schemas, powering production-ready systems for enterprise insights.

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