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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.

Design, build, and deploy LLM-powered validation pipelines and AI integrations for silicon design and post-silicon validation. Lead cross-team AI projects from prototype to production, evaluate emerging AI tech, and measure impact with data systems to improve workflows and scalability across chip development.

Build and scale robotics simulation and sim-to-real workflows for NVIDIA partners. Develop solutions involving robot rigging, physics, rendering, sensor simulation, synthetic data, GPU computing, and learned policies. Lead technical evaluations, proofs of concept, workshops, and reference implementations; diagnose performance and integration issues; validate hybrid physics and neural approaches; and collaborate with research, engineering, product, and customers to guide platform adoption and roadmaps.

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.

Architect, build, and deploy LLM-powered AI systems and automation to accelerate post-silicon validation and toolchain workflows; integrate AI across engineering teams, evaluate emerging frameworks, measure impact, and operationalize solutions from prototype to production while supporting silicon bring-up and lab debug.

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.

Design and optimize agentic AI systems for NVIDIA’s CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, orchestration, training, verification, reward, and evaluation systems. Co-design AI systems across models, inference, compilers, runtimes, libraries, kernels, and GPUs. The role requires strong C/C++, Rust, and Python skills, GPU programming expertise, and experience with agent systems, reinforcement learning, or modern AI inference.

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.

Design, build, and operate core cloud-native infrastructure services for NVIDIA DGX Cloud. Develop scalable, secure microservices and automation for provisioning and lifecycle management, integrate internal and external platforms, and implement observability and security to ensure reliable, global production operations.

Leads complex customer engagements for NVIDIA infrastructure services from planning through deployment, bring-up, support handover, and day-two readiness. Coordinates solution architects, delivery managers, sales, OEMs, operations, and support teams; provides executive status updates; manages risks, issues, blockers, scope, and commercial commitments; and drives resolution of critical customer and delivery concerns while maintaining technical oversight.