1 day ago
Base Salary
$152k - $288k/yr
Responsibilities
- Develop accelerated PyTorch-based solutions for large-scale GNN, tabular foundation, and ensemble models, including efficient GPU training and inference workflows.
- Support CUDA-X Libraries and their integrations with PyTorch-based machine learning workflows.
- Partner with developers, product managers, scientists, customers, and Solutions Architects to develop GPU-accelerated implementations and guide product priorities.
- Improve code architecture, reduce maintenance overhead, and use coding tools to fix bugs, implement features, and refactor code.
- Provide technical leadership and mentorship while solving complex issues and coordinating across teams.
Requirements
- Bachelor’s degree or equivalent experience plus 5 or more years of relevant experience, or a master’s degree or PhD plus 3 or more years of relevant experience.
- At least 3 years of experience with PyTorch and at least 2 years training enterprise-scale machine learning models across distributed infrastructure.
- At least 2 years designing and operating GPU training and inference workflows involving profiling, scaling, orchestration, and resource utilization.
- Excellent C++ programming, software design, debugging, optimization, and high-performance application development skills, preferably with CUDA acceleration.
- Strong collaboration, communication, and documentation skills.
- Preferred experience with Graph Neural Networks and PyTorch Geometric or a similar framework; Snowflake or Databricks; vector search and storage technologies such as FAISS or Milvus; customer engagement; and performance optimization across CPU, GPU, memory, and storage systems.
Benefits
- Eligible for equity and benefits.
- Applications are accepted at least until September 19, 2026.
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.