Senior Deep Learning Systems Engineer, Datacenters

About the role

The role of a Deep Learning Systems Engineer involves analyzing the performance and power consumption of deep learning applications on datacenter-class hardware and influencing the design and optimization of datacenters. This position aims to contribute to the development of high-performance Datacenters designed for the future of AI.

Responsibilities

• Help develop software infrastructure to characterize and analyze a broad range of Deep Learning applications.
• Evolve cost-efficient datacenter architectures tailored to meet the needs of Large Language Models (LLMs).
• Work with experts to help develop analysis and profiling tools in Python, bash, and C++ to measure key performance metrics of DL workloads running on Nvidia systems.
• Analyze system and software characteristics of DL applications.
• Develop analysis tools and methodologies to measure key performance metrics and estimate potential for efficiency improvement.

Requirements

• A Bachelor’s degree in Electrical Engineering or Computer Science or equivalent experience (Masters or PhD degree preferred).
• 8 years or more of relevant experience.
• Experience in at least one of the following:
• System Software: Operating Systems (Linux), Compilers, GPU kernels (CUDA), DL Frameworks (PyTorch, TensorFlow).
• Silicon Architecture and Performance Modeling/Analysis: CPU, GPU, Memory or Network Architecture.
• Experience programming in C/C++ and Python.
• Exposure to Containerization Platforms (docker) and Datacenter Workload Managers (slurm) is a plus.

Qualifications

• A deep understanding of computer system architecture and performance analysis is essential for success in this role.
• Demonstrated hands-on experience in these domains.
• Demonstrated ability to work in virtual environments, and a strong drive to own tasks from beginning to end.
• Prior experience with such environments will make you stand out.

Skills

• Background with system software, Operating system intrinsics, GPU kernels (CUDA), or DL Frameworks (PyTorch, TensorFlow).
• Experience with silicon performance monitoring or profiling tools (e.g. perf, gprof, nvidia-smi, dcgm).
• In depth performance modeling experience in any one of CPU, GPU, Memory or Network Architecture.
• Exposure to Containerization Platforms (docker) and Datacenter Workload Managers (slurm).
• Prior experience with multi-site teams or multi-functional teams.

Benefits

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative and autonomous, we want to hear from you!

Pay

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is $184,000 - $287,500 for Level 4, and $224,000 - $356,500 for Level 5.

Schedule

You will also be eligible for equity and benefits.

Application Instructions

Applications for this job will be accepted at least until May 11, 2026. This posting is for an existing vacancy.