Relational Foundation Model Engineer for Enterprise AI

Relational Foundation Model Engineer for Enterprise AI

Full-Time 80000 - 100000 Β£ / year (est.) No working from home possible
Nvidia

At a Glance

  • Tasks: Design and experiment with advanced ML architectures for enterprise AI.
  • Company: Join NVIDIA, a leader in cutting-edge technology and innovation.
  • Benefits: Attractive salary, health perks, and opportunities for professional growth.
  • Other info: Be part of a dynamic team driving the next wave of AI advancements.
  • Why this job: Shape the future of data with impactful AI solutions.
  • Qualifications: Experience in machine learning and strong collaboration skills.

The predicted salary is between 80000 - 100000 Β£ per year.

NVIDIA in the United Kingdom seeks a senior ML researcher to design and experiment with Transformer and GNN architectures that operate over relational schemas and heterogeneous graphs.

You will contribute to a foundational Relational Foundation Model that ships into production and shapes the modern data stack.

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

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Relational Foundation Model Engineer for Enterprise AI employer: Nvidia

NVIDIA is an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration among talented professionals. With a focus on cutting-edge technology in AI and cloud systems, employees benefit from competitive salary packages and ample opportunities for personal and professional growth in a dynamic environment. Join us to be part of a team that is not only solving significant challenges but also shaping the future of technology.

Nvidia

Contact Details:

Nvidia Recruitment Team

We think you need these skills to ace Relational Foundation Model Engineer for Enterprise AI

Machine Learning Research
Transformer Architectures
Graph Neural Networks (GNN)
Relational Schemas
Heterogeneous Graphs
Large-Scale Training
Post-Training Optimization