Senior System Software Engineer - AI Performance and Efficiency Tools
Used Tools & Technologies
Not specified
Required 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 @ 7
Kubernetes @ 4
Linux @ 4
Python @ 7
TensorFlow @ 4
Communication @ 7
Networking @ 4
Debugging @ 4
PyTorch @ 4
CUDA @ 4
GPU @ 4
Deep Learning @ 4
AI @ 4
Profiling @ 4
NCCL @ 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
A key part of NVIDIA's strength is our sophisticated analysis and debugging tools that empower NVIDIA engineers to improve performance and power efficiency of our products and the running applications. This role involves developing tools for AI researchers and software/hardware teams running AI workloads in GPU clusters. You will work with users from Architecture and Software teams to provide intuitive, rich, and accurate insight into workloads and systems to find opportunities in software and hardware, build high-level models, and debug tricky failures to improve performance and efficiency.
Responsibilities
- Build internal profiling and analysis tools for AI workloads at large scale
- Build debugging tools for common problems like memory and networking
- Create benchmarking and simulation technologies for AI systems or GPU clusters
- Partner with hardware architects to propose new features or improve existing features with real-world use cases
Requirements
- BS+ in Computer Science or related (or equivalent experience) and 6+ years of software development
- Strong software skills in design, coding (C++ and Python), analytical work, and debugging
- Good understanding of deep learning frameworks such as PyTorch and TensorFlow, and distributed training and inference
- Knowledge of GPU cluster job scheduling (Slurm or Kubernetes), storage, and networking
- Experience with NVIDIA GPUs, CUDA programming, and NCCL
- Motivated self-starter with strong problem-solving skills and customer-facing communication skills
- Passion for continuous learning and ability to work concurrently with multiple global groups
Ways to stand out from the crowd
- Proven experience in GPU cluster scale continuous profiling & analysis tools/platforms
- Solid experience in large AI job performance analysis for training/inference workloads
- Knowledge of Linux device drivers and/or compiler implementation
- Knowledge of GPU and/or CPU architecture and general computer architecture principles
Compensation & Benefits
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 (see NVIDIA benefits page).
Additional information
- #LI-Hybrid
- Applications for this job will be accepted at least until May 10, 2026. This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and does not discriminate on the basis of protected characteristics.