Senior ML Engineer — GPU-Accelerated PyTorch & GNNs

NVIDIA; California (MO); On-site; USD 152,000 - 242,000; Full time

Job summary

NVIDIA has a Senior Software Engineer opening to build large-scale ML solutions, including GNNs, TFMs, and ensembles, accelerating PyTorch frameworks and customer-facing tools. You will help shape high-performance training and inference workflows on NVIDIA GPU infrastructure.

You will collaborate with developers, PMs, and scientists, provide technical leadership, and drive performance optimizations across distributed systems.

Qualifications

• Bachelor’s degree (or equivalent) with 5+ years in ML or data science
• 3+ years of PyTorch experience
• 2+ years training enterprise-scale ML models on distributed infrastructure
• 2+ years designing and operating GPU-based training/inference workflows
• Excellent C++ programming and software design
• Strong collaboration, communication, and documentation habits

Responsibilities

• Develop accelerated PyTorch-based solutions for large-scale models on GPU infrastructure
• Support CUDA-X Libraries and integrations used in PyTorch workflows
• Partner with developers, PMs, and scientists on GNN models and GPU-accelerated implementations
• Gather technical requirements from customers and Solutions Architects to guide priorities
• Provide technical leadership and mentorship across the team
• Identify opportunities to improve the codebase and reduce maintenance via refactoring
• Apply agentic coding tools to fix bugs and implement new features
• Coordinate across multiple teams to achieve shared objectives

Skills

PyTorch

C++ programming

Collaboration

Communication

Documentation

Education

Bachelor's degree or equivalent

Master's degree or PhD (advantage)

Tools

CUDA

GPU acceleration

Job description

NVIDIA has a Senior Software Engineer opening to build large-scale ML solutions, including GNNs, TFMs, and ensembles, accelerating PyTorch frameworks and customer-facing tools. You will help shape high-performance training and inference workflows on NVIDIA GPU infrastructure.

You will collaborate with developers, PMs, and scientists, provide technical leadership, and drive performance optimizations across distributed systems.