System Software Engineer, Distributed Systems

Job Title

System Software Engineer, Distributed Systems

Role Summary

Join the VLSI Productivity and Infrastructure team to build and operate userspace systems that enable chip design engineers to run long-running, compute-heavy workflows on bare-metal Linux hosts. The role focuses on distributed systems, coordination over shared storage, orchestration with batch schedulers, reliability, performance, and incremental modernization of legacy codebases.

Experience Level

Mid-level. The role expects approximately 5+ years of relevant production software development and operations experience.

Responsibilities

Primary responsibilities involve designing, implementing, and operating core userspace infrastructure and orchestration for large-scale engineering workflows.

• Design, build, and deliver core components of productivity platforms for chip design workflows.
• Develop reliable userspace infrastructure for long-running workflows on bare-metal Linux hosts.
• Implement state coordination over NFS: atomicity, idempotency, partial-write recovery without privileged operations.
• Build orchestration around IBM LSF: submission/tracking, retries/cancel, log capture, fairness and backpressure.
• Incrementally migrate legacy codebases (for example, Perl to Go) with stage gates, parity strategies, and strong observability.
• Debug and improve performance and reliability across Linux and Kubernetes; develop operational tooling and observability.
• Collaborate with engineering users to convert ambiguous workflows into durable production systems and measurable delivery plans.

Requirements

Required skills and experience to perform the role; items under "Nice-to-have" indicate advantageous experience.

Must-have:

• 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 high-level goals into safe delivery plans with instrumentation, staged rollout, and measurable outcomes.

Nice-to-have:

• Hands-on experience with NFS or coordination patterns on eventually-consistent storage at scale.
• Experience with batch job scheduling, shared compute fleets, or large build systems.
• Track record of incremental modernization: tests, shadow runs, canaries, and rollback plans.
• Experience optimizing metadata-heavy systems and reducing I/O or R/W hotspots.
• Strong incident/debug tactics and rapid comprehension of unfamiliar codebases.

Education Requirements

B.S. in Computer Science or Electrical Engineering, or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.