Relational Foundation AI Engineer — Graph & Transformer Innovator

NVIDIA; Germany (OH); On-site; USD 150,000 - 190,000; Full time

Job summary

NVIDIA seeks a researcher for the Relational Foundation Model team to design and experiment with Transformer and GNN architectures that generalize across diverse relational schemas.

You will collaborate with researchers and engineers across the full ML lifecycle, from architecture exploration and large-scale training to post-training optimization and inference acceleration.

Strong background in ML, CS, Python, and PyTorch, plus at least 8 years of research experience in ML algorithms.

Qualifications

• MS or PhD in Machine Learning, Computer Science, or equivalent program.
• Proficiency in Python and deep learning frameworks, such as 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‑based models to operate over 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 your knowledge in ML and AI to tackle real challenges while contributing to scalable and adaptable solutions.
• Work may span the full lifecycle of modern ML systems: from architecture design/training to post‑training optimization and inference acceleration.
• You will contribute to our next generation of the Relational Foundation Model.

Skills

Python

PyTorch

Education

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

Job description

NVIDIA seeks a researcher for the Relational Foundation Model team to design and experiment with Transformer and GNN architectures that generalize across diverse relational schemas.

You will collaborate with researchers and engineers across the full ML lifecycle, from architecture exploration and large-scale training to post-training optimization and inference acceleration.

Strong background in ML, CS, Python, and PyTorch, plus at least 8 years of research experience in ML algorithms.