Relational Foundation Model Engineer, Modern Data Stack

NVIDIA•Santa Clara, CA
•$184,000 - $356,500

About The Position

NVIDIA is hiring an engineer for its Relational Foundation Model team, which is developing a unified foundation model for understanding structures, context, and relationships in relational databases and heterogeneous graphs. The engineer will design, build, and evaluate Transformer and graph neural network architectures that generalize across diverse data schemas. The work supports applications such as recommendation systems, demand forecasting, fraud detection, and predictive maintenance. The role involves working with researchers and engineers throughout the machine learning lifecycle, from architecture exploration and large-scale training through post-training optimization and high-performance inference. The engineer will contribute to the next generation of the Relational Foundation Model and help translate foundational research into production systems optimized for NVIDIA-accelerated infrastructure.

Requirements

  • MS or PhD in Machine Learning, Computer Science, or equivalent experience.
  • Proficiency in Python and deep learning frameworks such as PyTorch.
  • At least 8 years of research experience designing machine learning algorithm solutions.
  • Practical experience using predictive models in real-world applications.

Nice To Haves

  • Familiarity with graph-based machine learning.
  • Publications at venues such as NeurIPS, ICLR, ICML, or similar.

Responsibilities

  • Collaborate with researchers and engineers to enhance Transformer- and GNN-based models so they operate across relational schemas and heterogeneous graphs.
  • Contribute to high-impact applications such as forecasting, entity matching, customer retention, and fraud detection using an extensible foundation model.
  • Apply machine learning and AI expertise to real-world challenges and develop scalable, adaptable solutions.
  • Work across the machine learning systems lifecycle, including architecture design, training, post-training optimization, and inference acceleration.
  • Contribute to the next generation of the Relational Foundation Model.

Benefits

  • Equity
  • Benefits
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