Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. The VLSI Productivity and Infrastructure team supports 1000+ chip design engineers by building tools and platforms that supercharge their everyday work. Our mission: make chip designers faster. We build and operate long shelf-life systems spanning build automation, observability, analytics, automated error detection/remediation, and codebase modernization—with a strong commitment to stability. Our core workflow infrastructure runs as userspace software on bare-metal Linux hosts (no sudo, no containers). We coordinate shared state and artifacts via NFS, launch long-running, compute-heavy workflows on IBM LSF, and provide adjacent services for APIs and observability. This is a high-ownership environment where you'll often be the expert on what you build. We are looking for a pragmatic and versatile systems engineer who enjoys working near the metal and building tools that empower other engineers. This is a generalist role with an emphasis on distributed systems and operational excellence in a “below containers” world: coordination, reliability, performance, and safe evolution of legacy systems (including incremental modernization of large codebases into Go). This isn't a CI/CD pipeline configuration role; you will be writing the userspace software that manages state, concurrency, and reliability at scale. What you will be doing: • Design, build, and deliver core components of our next-generation productivity platforms • Develop reliable userspace infrastructure for long-running engineering workflows at scale on bare-metal Linux hosts • Build state coordination over NFS (atomicity, idempotency/dedup, partial-write recovery, without privileged ops) • Build and improve orchestration around IBM LSF (submission/tracking, retries/cancel, log capture, fairness/backpressure) • Convert legacy codebases into modern powerhouses using incremental migration techniques (e.g., Perl to Go), with stage gates, parity strategies, and strong observability • Debug and improve performance and reliability across Linux and Kubernetes, including operational tooling • Collaborate with engineering users to turn ambiguous workflows into durable production systems What we need to see: • B.S. CS/EE (or equivalent experience) • 5+ years developing and operating production software in Go and/or Python, ideally in large codebases • Strong Linux fundamentals: processes, filesystems, permissions, synchronization/locks, concurrency, and debugging • Solid distributed-systems thinking: failures, retries/timeouts, backoff, idempotency, and operational rigor • Experience building long-runtime automation or services on shared compute clusters (batch schedulers, build systems) • Ability to translate ambitious, high-level goals into a safe delivery plan (instrumentation, staged rollout, measurable outcomes) Ways to stand out from the crowd: • Hands-on experience with shared filesystems at scale (NFS), or coordination patterns on eventually-consistent storage • Experience with batch job scheduling, shared compute fleets, or build systems • Track record of incremental modernization (tests, shadow runs, canaries, rollback plans) • Experience partitioning/optimizing metadata-heavy systems and reducing I/O or R/W hot spots • Strong incident/debug tactics: clear root-cause analysis, remediation, and guardrails as well as rapid comprehension and ownership of unfamiliar codebases in any language (including LLM-generated code) to implement high-leverage changes With competitive salaries and a generous benefits package, we are 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 and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 10, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Design, build, and deliver core components of our next-generation productivity platforms
Develop reliable userspace infrastructure for long-running engineering workflows at scale on bare-metal Linux hosts
Build state coordination over NFS (atomicity, idempotency/dedup, partial-write recovery, without privileged ops)
Build and improve orchestration around IBM LSF (submission/tracking, retries/cancel, log capture, fairness/backpressure)
Convert legacy codebases into modern powerhouses using incremental migration techniques (e.g., Perl to Go), with stage gates, parity strategies, and strong observability
Debug and improve performance and reliability across Linux and Kubernetes, including operational tooling
Collaborate with engineering users to turn ambiguous workflows into durable production systems
B.S. CS/EE (or equivalent experience)
5+ years developing and operating production software in Go and/or Python, ideally in large codebases
Strong Linux fundamentals: processes, filesystems, permissions, synchronization/locks, concurrency, and debugging
Solid distributed-systems thinking: failures, retries/timeouts, backoff, idempotency, and operational rigor
Experience building long-runtime automation or services on shared compute clusters (batch schedulers, build systems)
Ability to translate ambitious, high-level goals into a safe delivery plan (instrumentation, staged rollout, measurable outcomes)
Hands-on experience with shared filesystems at scale (NFS), or coordination patterns on eventually-consistent storage
Experience with batch job scheduling, shared compute fleets, or build systems
Track record of incremental modernization (tests, shadow runs, canaries, rollback plans)
Experience partitioning/optimizing metadata-heavy systems and reducing I/O or R/W hot spots
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until October 10, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigoci/cdkubernetespythonllm

Platform: eightfold
Effort: medium

Benefits

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