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The Senior Production Engineer will scale AI infrastructure, manage production systems, implement monitoring capabilities, and ensure reliability and performance across NVIDIA's AI clusters.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

Provide Tier 1 support for complex cloud platforms, troubleshoot distributed software and customer issues, investigate root causes, improve operational workflows, create runbooks and documentation, build support tooling, and coordinate with engineering, SRE, and other internal teams. The role supports production systems through an on-call rotation and requires expertise across cloud infrastructure, networking, storage, Kubernetes, Linux, and DevOps tooling, with GPU, MLOps, HPC, or SLURM experience preferred.

Develops strategic relationships and revenue growth with universities and higher-education research institutions. Owns the complete sales cycle, from prospecting and discovery through negotiation and closing, while meeting quotas and forecasting revenue. Creates territory and account plans, translates complex software and computing requirements into NVIDIA solutions, and collaborates with solution architects, product teams, developer relations, business units, and channel partners. Requires consultative technology sales expertise and understanding of AI, accelerated computing, and research environments.

Design, develop, test, and extend NVIDIA’s large-scale object storage platform. Responsibilities include improving availability, reliability, durability, fault tolerance, and performance across deployments exceeding 10,000 nodes and exabytes of data. The role also automates storage infrastructure provisioning, management, and monitoring; supports AI/ML research teams; owns product delivery through support; and contributes to open-source storage projects.

Lead design, automation, and operation of a scalable, secure Linux platform across physical, virtual, and cloud environments. Build IaC, provisioning, patching, security hardening, and lifecycle automation. Deploy and operate cloud infrastructure, integrate hybrid environments, and collaborate with engineering teams to improve platform reliability and maintainability.

Sell NVIDIA's networking portfolio to enterprise accounts, driving quota attainment by identifying, qualifying, and closing opportunities. Lead customer conversations across business and technical stakeholders, collaborate with solution architects and partners, and support solution development for AI and data center initiatives. Maintain pipeline, forecasting, and opportunity tracking in Salesforce while coordinating complex sales motions and building strategic customer relationships across Physical AI and Financial Services industries.

Deploy, manage, validate, and optimize large-scale AI compute and HPC infrastructure in Linux environments. Serve as a technical liaison among NVIDIA, partners, and enterprise customers; lead planning through implementation, document handoffs, transfer knowledge, troubleshoot hardware and software, and provide feedback for product and architecture improvements. The role involves networking, system design, automation, benchmarking, cluster provisioning, and customer-site travel up to 30%.

Own NVIDIA’s config-as-code foundation for its EDA compute farm. Design LSF deployment schemas, build federated deployment pipelines with staged rollout and rollback, eliminate configuration drift, and create regression testing for scheduled LSF upgrades. Partner with an LSF specialist to encode scheduler expertise into templates and policies. The role also requires infrastructure automation, GitOps at scale, tooling development in Go, Python, and shell, and management of stateful production infrastructure.