Turn expert verification practices into repeatable and measurable workflows, integrating agents with source control, requirements, CI/CD, issue tracking, lab orchestration, and observability systems while defining test oracles, guardrails, human approval points, evaluation, and tracing.
What we need to see
B.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
3+ years of software engineering experience, with significant hands-on work in test automation, system verification, test infrastructure, or related areas.
Deep, production-grade Python expertise, including type-safe data modeling, clear APIs and tool contracts, automated testing, static analysis, performance-aware implementation, and mechanisms for validating and monitoring AI-generated code.
Experience testing complex product features and building production-quality automation, CI pipelines, and regression environments, together with strong debugging skills and knowledge of Linux, Git, APIs, containers, and modern software engineering practices.
Hands-on experience building reliable LLM-based or agentic workflows beyond the prototype stage, including tool integration, code generation or modification, execution and feedback loops, controlled corrective actions, evaluation, guardrails, observability, context management, and prompt or model versioning.
Ways to stand out from the crowd
Experience with networking, telemetry, distributed systems, data center infrastructure, or hardware and software integration.
Experience building system-level verification frameworks, large regression environments, test labs, topology orchestration, or traffic-generation systems.
Experience validating high-scale data ingestion, observability pipelines, metrics, logs, traces, or other telemetry-intensive systems.
Experience with agent-driven change-impact analysis, test selection, failure clustering, root-cause analysis, automated debugging, or flaky-test management.
A track record of turning engineering or AI prototypes into reliable capabilities adopted by multiple engineers or teams, combined with a strong systems mindset and a practical approach to establishing correctness.
With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you are passionate about building mission-critical systems at the frontier of AI infrastructure, we want to hear from you.
