As NVIDIA makes inroads into the Datacenter business, our team plays a central role in getting the most out of our exponentially growing datacenter deployments as well as establishing a data-driven approach to hardware design and system software development. The role of a Deep Learning Systems Engineer would be to analyze the performance and power consumption of deep learning applications on datacenter-class hardware and significantly influence the design and optimization of datacenters.
Do you want to influence the development of high-performance Datacenters designed for the future of AI? Do you have an interest in system architecture and performance? In this role you will find how CPU, GPU, networking, and IO relate to deep learning (DL) architectures for Natural Language Processing, Computer Vision, Autonomous Driving and other technologies. Come join our team, and bring your interests to help us optimize our next generation systems and Deep Learning Software Stack.
What you'll be doing:
Help develop software infrastructure to characterize and analyze a broad range 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 to estimate potential for efficiency improvement.
What we need to see:
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.
A deep understanding of computer system architecture and performance analysis is essential for success in this role. Applicants should have 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.
Ways to stand out from the crowd:
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.
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!
#LI-Hybrid
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 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Top Skills
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
