NVIDIA's Performance Lab (PerfLab) builds the systems and automation used to evaluate the performance and quality of accelerated computing and AI workloads. We turn complex benchmark experiments into reliable, scalable, and reproducible workflows that help engineering teams make better decisions faster.
We are looking for an experienced and highly self-motivated System Software Engineer to help build the next generation of PerfLab's benchmark infrastructure. You will independently own meaningful platform components and take projects from problem discovery and technical design through production deployment and adoption. The ideal candidate enjoys finding important engineering problems, understanding their root causes, and using technology to create simple, reusable solutions.
You will collaborate with NVIDIA teams around the world and work on evolving areas such as large language models, agentic AI, accelerated computing, and other emerging AI workloads.
What You'll Be Doing:
Design, build, and maintain reusable software, services, and workflows that automate benchmark definition, execution, result collection, validation, and reporting across local, cluster, and cloud-native environments.
Carry out performance testing and analysis as needed. Develop a deep understanding of existing performance workflows and infrastructure, and translate real-world needs into scalable, user-friendly solutions.
Improve the reliability, scalability, observability, and reproducibility of large benchmark campaigns.
Diagnose complex issues across applications, Linux systems, containers, distributed jobs, compute resources, networking, and storage.
Build strong partnerships with performance engineers, QA teams, product teams, and other customers; agree on goals, organize execution, and drive new ideas and projects from concept through adoption.
Contribute to technical designs, code reviews, documentation, and internal or open-source infrastructure projects.
Apply AI-assisted automation where it can meaningfully improve benchmark creation, failure triage, data analysis, or engineering productivity.
What We Need to See:
Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience in practice.
5+ years of relevant software engineering experience in system software, infrastructure, developer platforms, distributed systems, or production automation.
Strong Python programming and software engineering skills, with experience building production-quality tools, services, or automation frameworks.
Solid understanding of Linux and system-level concepts such as processes, concurrency, networking, storage, resource management, and failure handling.
Hands-on experience with containers and at least one workload orchestration, scheduling, or distributed computing platform, as well as designing reliable systems or pipelines with clear interfaces, testing, observability, and recovery behavior.
Working knowledge of machine learning, AI, or accelerated-computing workloads and an interest in how their performance and quality are evaluated.
Strong analytical and problem-solving abilities, with the capacity to manage multiple priorities effectively and adapt in a dynamic, fast-changing environment.
High self-motivation and demonstrated ability to identify valuable problems, turn ambiguous needs into clear technical plans, and drive projects through delivery and adoption.
Excellent communication and organizational skills, with the ability to align cross-functional stakeholders, collaborate with globally distributed teams, and move new ideas toward concrete outcomes.
Ways to Stand Out From the Crowd:
Experience with GPU or AI infrastructure, distributed training or inference, model evaluation, or performance benchmarking.
Experience building workflow engines, schedulers, experiment platforms, test frameworks, or developer infrastructure.
Experience operating distributed or cloud-native systems in production, including performance profiling, capacity analysis, resource scheduling, or multi-node workloads.
Practical experience using AI agents, tool-calling, or coding agents to automate engineering workflows.
Contributions to open-source infrastructure or developer tooling, or a track record of initiating automation that reduced manual work, improved reliability, or helped other teams move faster.
We have some of the most forward-thinking and hardworking people in the world working for us. If you are creative, autonomous, and passionate about building systems that make complex AI performance work repeatable and scalable, we want to hear from you.
Skills Required
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent experience
- 5+ years of relevant software engineering experience in system software, infrastructure, developer platforms, distributed systems, or production automation
- Strong Python programming and software engineering skills, experience building production-quality tools, services, or automation frameworks
- Solid understanding of Linux and system-level concepts (processes, concurrency, networking, storage, resource management, failure handling)
- Hands-on experience with containers and at least one workload orchestration, scheduling, or distributed computing platform
- Working knowledge of machine learning, AI, or accelerated-computing workloads
- Strong analytical and problem-solving abilities and ability to manage multiple priorities
- High self-motivation, ability to translate ambiguous needs into technical plans and drive projects to delivery and adoption
- Excellent communication and organizational skills for aligning cross-functional, globally distributed teams
- Experience with GPU or AI infrastructure, distributed training or inference, model evaluation, or performance benchmarking
- Experience building workflow engines, schedulers, experiment platforms, test frameworks, or developer infrastructure
- Experience operating distributed or cloud-native systems in production, including performance profiling, capacity analysis, or resource scheduling
- Practical experience using AI agents or automating engineering workflows with agents/tool-calling
- Contributions to open-source infrastructure or a track record of impactful automation initiatives
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.”
