Senior Performance Engineer: AI Workload Optimization

NVIDIA; Redmond (WA); On-site; USD 224,000 - 431,250; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA seeks a Senior Performance Engineer to profile and optimize large-scale AI workloads within the DGX Cloud AI Efficiency Team in Redmond, WA. You will characterize workloads, establish baselines, diagnose bottlenecks, and drive end-to-end improvements from investigation to deployment.

You'll collaborate with deep learning engineers, platform teams, and GPU architects to translate measurements into actionable design decisions and measurable performance gains.

Qualifications

• BS or higher degree in computer science, computer engineering, or a related field.
• 12+ years of experience with strong programming in C++ and Python.
• Solid foundation in operating systems, computer architecture, and distributed systems.
• Experience with performance engineering, benchmarking, profiling, and optimization of complex software or systems.
• Ability to communicate technical findings and prioritize high-impact work across teams.

Responsibilities

• Analyze end-to-end performance of large-scale AI workloads across compute, network, storage, and software stacks.
• Design and execute performance studies to establish baselines, diagnose regressions, and quantify bottlenecks.
• Define evaluation methodologies, benchmarks, and success metrics for AI workloads.
• Use profiling, observability, and data analysis to translate measurements into optimization plans.
• Partner with deep learning engineers, platform teams, and GPU architects to validate and deliver improvements.
• Communicate performance findings, tradeoffs, and recommendations clearly to influence design decisions.

Skills

C++

Python

Performance engineering

Education

Bachelor's degree in Computer Science or Computer Engineering

Tools

CUDA

PyTorch

JAX/XLA

Job description

NVIDIA seeks a Senior Performance Engineer to profile and optimize large-scale AI workloads within the DGX Cloud AI Efficiency Team in Redmond, WA. You will characterize workloads, establish baselines, diagnose bottlenecks, and drive end-to-end improvements from investigation to deployment.

You'll collaborate with deep learning engineers, platform teams, and GPU architects to translate measurements into actionable design decisions and measurable performance gains.