Relational Foundation Model Engineer, Modern Data Stack

Nvidia

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$184,000–$287,500 / yr
Employment
Full-time
Posted
71 days ago
Freshness
Confirmed live today

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $219k
This role $236k
$172k $300k
below market most similar roles pay here above market

This role pays more than 70% of similar roles. Most pay $192,050–$246,150 — the blue band above. At the midpoint, this role pays about $236k versus about $219k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Relational Foundation Model Engineer, Modern Data Stack

As a Relational Foundation Model Engineer on the Modern Data Stack team, you will design, build, and evaluate novel Transformer and graph neural network architectures. You will work to create a unified foundation model capable of understanding structure, context, and relationships within relational databases and heterogeneous graphs. Your daily responsibilities include collaborating with researchers across the full machine learning lifecycle, from architecture exploration and large-scale training to post-training optimization and high-performance inference. You will use Python and deep learning frameworks like PyTorch to solve complex problems in demand forecasting, fraud detection, and predictive maintenance. This role focuses on developing scalable solutions for relational reasoning and graph learning to help organizations derive intelligence from complex, connected data and diverse schemas.

What you'll do

  • Design, build, and evaluate novel Transformer and graph neural network architectures.
  • Develop models that generalize across diverse relational schemas and heterogeneous graphs.
  • Manage the full machine learning lifecycle from architecture exploration to large-scale training.
  • Perform post-training optimization and high-performance inference acceleration.
  • Build production systems for use cases like forecasting, entity matching, and fraud detection.
  • Optimize foundation models for NVIDIA-accelerated infrastructure.
  • Translate advanced AI research into scalable and deployable production systems.

What we're looking for

  • 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 in designing ML algorithm solutions.
  • Practical experience using predictive models in real-world applications.
  • Familiarity with graph-based machine learning (preferred).
  • Publications at venues such as NeurIPS, ICLR, ICML, or similar (preferred).

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