Relational Foundation Model Engineer, Modern Data Stack

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Posted: 25 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Competitive pay

How this pay compares to similar roles

Similar $208k

This role $236k

$142k most similar roles pay here $303k

This role pays more than 65% of similar roles. Most pay $161,250–$254,750 — the shaded band above. At the midpoint, this role pays about $236k versus about $208k for comparable roles.

Based on 240 similar postings.

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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 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

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

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Relational Foundation Model Engineer, Modern Data Stack is a role within the Relational Foundation Model team focused on developing unified models that understand structure and context in relational databases and heterogeneous graphs. You will design, build, and evaluate novel Transformer and graph neural network architectures to generalize across diverse data schemas for applications like recommendation systems, demand forecasting, fraud detection, and predictive maintenance. Working across the full machine learning lifecycle, you will engage in architecture exploration, large-scale training, post-training optimization, and high-performance inference. The role requires proficiency in Python and deep learning frameworks such as PyTorch, along with extensive research experience in designing machine learning algorithm solutions. You will solve complex problems involving relational reasoning and graph learning to move beyond single-table benchmarks by building scalable systems that derive intelligence from complex, connected data.

Skills

Python PyTorch Transformer Machine Learning Deep Learning Inference Acceleration Relational Databases Heterogeneous Graphs Large-scale Training Post-training Optimization

What you'll do

Design and build novel Transformer and graph neural network architectures for diverse data schemas.
Develop models that understand structure, context, and relationships within relational databases and heterogeneous graphs.
Execute the full machine learning lifecycle including architecture exploration, large-scale training, and post-training optimization.
Optimize models for high-performance inference on NVIDIA-accelerated infrastructure.
Build scalable solutions for use cases like recommendation systems, demand forecasting, and fraud detection.
Develop a unified foundation model capable of generalizing across various relational schemas.
Translate advanced AI research into production-ready systems for enterprise applications.

What we're looking for

MS or PhD in Machine Learning, Computer Science, or equivalent experience.
Proficiency in Python and deep learning frameworks like 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).