Used Tools & Technologies
NLP LLMRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Software Development @ 4
Docker @ 4
Linux @ 4
Python @ 4
TensorFlow @ 4
Bash @ 4
Networking @ 6
Performance Monitoring @ 4
System Architecture @ 6
PyTorch @ 4
CUDA @ 4
GPU @ 6
Deep Learning @ 6
AI @ 6
Computer Vision @ 6
Profiling @ 4
Slurm @ 4
Performance Analysis @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
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.
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 to estimate potential for efficiency improvement.
Requirements
- Bachelor’s degree in Electrical Engineering or Computer Science or equivalent experience (Masters or PhD 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; applicants should have demonstrated hands-on experience in these domains.
- Demonstrated ability to work in virtual/multi-site or multi-functional teams and to own tasks from beginning to end.
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.
Benefits
- Base salary (location- and level-dependent). The posting lists base salary ranges: 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
- Eligibility for equity and additional benefits (link provided in original posting).
Other notes: #LI-Hybrid
Applications for this job will be accepted at least until May 11, 2026. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer.