We are seeking a Senior AI/ML Performance and Efficiency Engineer, GPU Clusters at NVIDIA to join our AI Efficiency efforts. As an Engineer, you will have a pivotal role in enhancing efficiency for our researchers by implementing progressions throughout the entire stack. Your main task will revolve around collaborating closely with customers to pinpoint and address infrastructure and application deficiencies, facilitating groundbreaking AI and ML research on GPU Clusters. Together, we can craft potent, effective, and scalable solutions as we mold the future of AI/ML technology!
What you will be doing:
Collaborate closely with our AI/ML researchers to make their ML models more efficient leading to significant productivity improvements and cost savings
Build tools, frameworks, and apply ML techniques to detect & analyze efficiency bottlenecks and deliver productivity improvements for our researchers
Work with researchers working on a variety of innovative ML workloads across Robotics, Autonomous vehicles, LLM’s, Videos and more
Collaborate across the engineering organizations to deliver efficiency in our usage of hardware, software, and infrastructure
Proactively monitor fleet wide utilization patterns, analyze existing inefficiency patterns, or discover new patterns, and deliver scalable solutions to solve them
Keep up to date with the most recent developments in AI/ML technologies, frameworks, and successful strategies, and advocate for their integration within the organization.
What we need to see:
BS or similar background in Computer Science or related area (or equivalent experience)
Minimum 5+ years of experience designing and operating large scale compute infrastructure
Strong understanding of modern ML techniques and tools
Experience investigating, and resolving, training & inference performance end to end
Debugging and optimization experience with NSight Systems and NSight Compute
Experience with debugging large-scale distributed training using NCCL
Proficiency in programming & scripting languages such as Python, Go, Bash, as well as familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) in addition to experience with parallel computing frameworks and paradigms.
Dedication to ongoing learning and staying updated on new technologies and innovative methods in the AI/ML infrastructure sector.
Excellent communication and collaboration skills, with the ability to work effectively with teams and individuals of different backgrounds
Ways to stand out from the crowd:
Background with NVIDIA GPUs, CUDA Programming, NCCL and MLPerf benchmarking
Experience with Machine Learning and Deep Learning concepts, algorithms and models
Familiarity with InfiniBand with IBOP and RDMA
Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads
Familiarity with deep learning frameworks like PyTorch and TensorFlow
NVIDIA offers competitive salaries and a comprehensive benefits package. Our engineering teams are growing rapidly due to outstanding expansion. If you're a passionate and independent engineer with a love for technology, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.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.Skills Required
- BS in Computer Science or related field or equivalent experience
- 5+ years designing and operating large-scale compute infrastructure
- Strong understanding of modern ML techniques and tools
- Experience investigating and resolving training and inference performance end to end
- 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 platforms (AWS, GCP, Azure)
- Experience with parallel computing frameworks and paradigms
- Excellent communication and collaboration skills
- Background with NVIDIA GPUs, CUDA programming, and MLPerf benchmarking
- Experience with machine learning and deep learning concepts, algorithms, and models
- Familiarity with InfiniBand (IBOP) and RDMA
- Understanding of distributed storage systems like Lustre and GPFS
- Familiarity with PyTorch and TensorFlow
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
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.”
