System Software Engineer - Performance Lab

NVIDIA•Santa Clara, CA

1d•$184,000 - $356,500

About The Position

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. NVIDIA's invention of the GPU 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU computing ignited the era of AI. NVIDIA is constantly evolving by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world. We are looking for you to join NVIDIA’s Performance Lab where you will be encouraged to craft and build outstanding software solutions that challenge NVIDIA products in new ways. Our team values passion, and positive interactions with teammates. We offer the opportunity to work on cutting edge technologies in the fields of AI, Graphics Rendering, and Datacenters.

Requirements

Bachelors degree in Computer Engineering, Software Engineering, Computer Science, or related field (or equivalent experience) with 8+ years of experience.
Desire to improve code quality by learning and applying computer science fundamentals, algorithms, and data structures.
Comfort with teamwork, collaboration, and a desire to reach across functional borders to develop new partnerships.
Professional experience with Python.
Working comfort in a Linux command-line environment with version control.
Foundational understanding and interest of the machine learning lifecycle (training, evaluation, and inference).

Nice To Haves

Familiarity with PyTorch and/or training, testing, and evaluating machine learning models.
Experience with GPU computing or CUDA and libraries like cuOPT, CUTLASS, cuDNN, etc.
Exposure to workload orchestration and job schedulers (Kubernetes, Slurm).
Experience with containerized applications and resource management.
Interest in quantitative finance and applying performance data to real-world problems.

Responsibilities

Writing and maintaining containerized GPU accelerated workloads for the financial services industry, from deep learning training and inference, to portfolio optimization and backtesting.
Running, validating, and analyzing benchmarking models at scale on HPC clusters.
Visualizing performance data, building charts and dashboards using internal schemas and tooling.
Working closely with the latest and greatest in financial AI models and tooling to help build reference models for NVIDIA.