Manager, System Software Engineering - Infrastructure Engineering and Automation

NVIDIA has transformed computer graphics, PC gaming, and accelerated computing for more than 25 years, driven by groundbreaking technology and exceptional people. Today, we’re shaping the next era of computing with AI—from research and model development to deployment and everyday applications.

We are seeking an experienced, technically strong engineering leader to build and lead infrastructure and validation engineering teams supporting NVIDIA’s Local AI and PC gaming R&D. These teams develop the software infrastructure, test automation, and debugging tools that enable reliable validation at scale. They also operate NVIDIA’s largest hardware cluster dedicated to these domains, ensuring lab readiness, hardware health, and system recovery to keep engineering teams productive. In this role, you will define the infrastructure and validation strategy for current and next-generation platforms, including RTX PCs, RTX Spark, DGX Spark, RTX PRO systems, and DGX Station. You will lead the development of scalable automation and reliable infrastructure to validate AI inference frameworks, agents, kernel libraries, and DirectX gaming drivers—helping engineering teams move faster and deliver high-quality products.

What you’ll be doing:

• Lead and grow infrastructure and validation engineering teams responsible for CI/CD, test development and automation, regression analysis, failure triage and debugging support, and lab operations, including hardware health, maintenance, and recovery.
• Define and drive the team’s technical roadmap, priorities, and delivery commitments across Local AI and PC gaming, balancing long-term infrastructure improvements with day-to-day engineering needs.
• Drive the development and deployment of tests and benchmarks that reflect real-world usage, supported by automation for environment setup, test execution, log collection, and results analysis.
• Build tools that help reproduce failures and analyze logs and system metrics. Partner with engineering teams to identify root causes and resolve functional issues and performance bottlenecks across the hardware and software stack.
• Ensure build systems, CI pipelines, test orchestration, and hardware labs are reliable and scalable. Plan staffing, hardware capacity, schedules, and investments with infrastructure, security, and operations partners.
• Communicate strategy, progress, risks, and technical trade-offs to engineering teams and senior leadership.
• Partner with graphics, driver, platform, Local AI, product, and open-source teams to define requirements, align priorities, resolve dependencies, and deliver infrastructure that accelerates engineering productivity.

What we need to see:

• Bachelor's or master's degree, in Computer Engineering, Computer Science, Electrical Engineering, or a related technical field, or equivalent experience.
• Min 3 years of exp in management and over all 10+ years of relevant software engineering experience, including substantial system software, platform, runtime, or systems infrastructure development.
• Proven record of managing large, high-performing software teams, setting direction, developing engineers, and delivering results through teams.
• In-depth knowledge of system programming and operating systems concepts, including APIs, processes and threads, concurrency, memory and resource management, and software–hardware interaction.
• Experience designing, debugging, profiling, or optimizing software across system boundaries, using instrumentation and performance data to improve correctness, reliability, and efficiency.
• Experience setting technical strategy and operational goals, leading multi-team engineering programs, and balancing staffing, capacity, schedules, and investments.
• Ability to communicate technical decisions to senior stakeholders and align partners across software and hardware disciplines.

Ways to stand out from the crowd:

• Background with graphics software, GPU or device-driver stacks, DirectX, or Windows platform bring-up.
• Experience integrating or debugging local AI inference frameworks such as llama.cpp, Ollama, PyTorch, vLLM, or Windows ML; familiarity with CUDA or GPU programming is a plus.
• Experience building system software, runtimes, developer tools, test frameworks, or benchmark infrastructure used across diverse hardware configurations.
• Experience with system profiling, performance optimization, telemetry, diagnostics, or analysis of CPU and GPU workload behavior.
• Contributions to open-source software or experience supporting developer-facing software infrastructure and hardware-backed validation.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.