Senior AI Performance and Efficiency Engineer

at Nvidia

USD 152,000-287,500 per year

SENIOR

✅ On-site

Tech Stack
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.

AI @ 4 AWS @ 3 Algorithms @ 4 Azure @ 3 Bash @ 6 CUDA @ 4 Cloud Computing @ 3 Communication @ 6 Debugging @ 4 Deep Learning @ 4 GCP @ 3 GPU Go @ 6 HPC @ 4 InfiniBand @ 3 LLM Machine Learning @ 7 NCCL @ 4 PyTorch @ 3 Python @ 6 Robotics TensorFlow @ 3

Details

NVIDIA is seeking a Senior AI/ML Performance and Efficiency Engineer focused on GPU clusters to support AI efficiency initiatives. The role involves improving efficiency across the technology stack, collaborating with researchers and engineering teams, and developing scalable solutions for AI and machine learning workloads.

Responsibilities

Collaborate with AI/ML researchers to improve model efficiency, productivity, and cost savings.
Build tools and frameworks and apply machine learning techniques to detect and analyze efficiency bottlenecks.
Support innovative machine learning workloads across robotics, autonomous vehicles, large language models, video, and other areas.
Collaborate across engineering organizations to improve the efficiency of hardware, software, and infrastructure usage.
Monitor fleet-wide utilization patterns, analyze inefficiencies, identify new patterns, and deliver scalable solutions.
Stay current with developments in AI/ML technologies, frameworks, and efficiency strategies, and advocate for their adoption.

Requirements

Bachelor's degree or equivalent background in Computer Science or a related area, or equivalent experience.
At least 5 years of experience designing and operating large-scale compute infrastructure.
Strong understanding of modern machine learning techniques and tools.
Experience investigating and resolving end-to-end training and inference performance issues.
Debugging and optimization experience with Nsight Systems and Nsight Compute.
Experience debugging large-scale distributed training using NCCL.
Proficiency in Python, Go, and Bash.
Familiarity with cloud computing platforms such as AWS, GCP, and Azure.
Experience with parallel computing frameworks and paradigms.
Commitment to ongoing learning about technologies and methods in AI/ML infrastructure.
Excellent communication and collaboration skills.

Preferred Qualifications

Experience with NVIDIA GPUs, CUDA programming, NCCL, and MLPerf benchmarking.
Knowledge of machine learning and deep learning concepts, algorithms, and models.
Familiarity with InfiniBand, IBOP, and RDMA.
Understanding of distributed storage systems such as Lustre and GPFS for AI/HPC workloads.
Familiarity with PyTorch and TensorFlow.

Compensation and Benefits

Base salary range: $152,000–$241,500 USD for Level 3.
Base salary range: $184,000–$287,500 USD for Level 4.
Compensation depends on location, experience, and pay for similar positions.
Eligible employees also receive equity and benefits.
Applications will be accepted at least until March 23, 2026.
NVIDIA is an equal opportunity employer and uses AI tools in its recruiting processes.