Senior AI Performance and Efficiency Engineer

Nvidia

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CANew York, NYSeattle, WA
Salary: $152,000–$241,500 / yr
Posted: 176 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $204k

This role $197k

$140k most similar roles pay here $265k

This role pays less than 56% of similar roles. Most pay $162,000–$246,150 — the shaded band above. At the midpoint, this role pays about $197k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior AI Performance and Efficiency Engineer

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As a Senior AI Performance and Efficiency Engineer, you will join the AI Efficiency team to enhance performance across the entire stack for researchers using GPU Clusters. You will collaborate with researchers to identify infrastructure and application deficiencies while building tools and frameworks to detect and analyze efficiency bottlenecks in diverse workloads including Robotics, Autonomous vehicles, LLMs, and Video. Your daily work involves monitoring fleet-wide utilization patterns and delivering scalable solutions to improve hardware, software, and infrastructure usage. To succeed, you must possess expertise in Python, Go, Bash, and cloud platforms like AWS, GCP, or Azure. You will utilize tools such as NSight Systems, NSight Compute, and NCCL for distributed training. Preferred skills include CUDA programming, MLPerf benchmarking, InfiniBand with RDMA, and experience with PyTorch, TensorFlow, Lustre, or GPFS storage systems.

Skills

Python Go Bash CUDA NCCL PyTorch TensorFlow NSight Systems NSight Compute AWS GCP Azure InfiniBand RDMA Lustre GPFS MLPerf Distributed Training Parallel Computing Machine Learning

What you'll do

Identify and resolve infrastructure and application deficiencies to improve ML model efficiency.
Build tools and frameworks to detect and analyze performance bottlenecks in GPU clusters.
Optimize training and inference performance across diverse workloads like LLMs, robotics, and autonomous vehicles.
Monitor fleet-wide utilization patterns to identify and solve large-scale inefficiency problems.
Develop scalable solutions to improve the usage of hardware, software, and infrastructure.
Debug and optimize distributed training using NCCL and tools like NSight Systems.
Advocate for and integrate the latest AI/ML technologies and frameworks into organizational workflows.

What we're looking for

BS or similar background in Computer Science or a related field (or equivalent experience).
Minimum 5+ years of experience designing and operating large scale compute infrastructure.
Strong understanding of modern ML techniques, tools, and deep learning concepts.
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, or Bash scripting and familiarity with cloud computing platforms like AWS, GCP, or Azure.
Familiarity with parallel computing frameworks, CUDA programming, and deep learning frameworks like PyTorch or TensorFlow.