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Leads a globally distributed customer engineering organization supporting internal EDA infrastructure while overseeing a Kubernetes platform team. Responsibilities include developing managers, improving support operations, defining SLOs, translating support trends into engineering roadmaps, automating recurring operational work, and advancing AI/LLM-based support assistants. The role requires hands-on technical engagement with Kubernetes, EDA job schedulers, internal LLM infrastructure, and production environments.

Develop and optimize large-scale data ingestion and serving infrastructure for autonomous vehicle systems. Build reliable pipelines, real-time and batch data services, validation checks, monitoring, and scalable distributed systems. Collaborate with AV development teams to improve data processing efficiency, latency, availability, and quality while documenting ingestion processes.

The Senior Localization Engineer will build robust localization solutions using navigation maps for autonomous vehicles, ensuring real-time performance and adherence to automotive safety standards.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Leads technical developer-relations strategy for NVIDIA platforms across capital markets. Advises quants, traders, developers, financial institutions, ISVs, and cloud providers; demonstrates CUDA, CUDA-X, AI frameworks, and accelerated computing solutions; supports partner onboarding and integrations; develops benchmarks, sample code, and reference implementations; gathers ecosystem feedback; influences product roadmaps; and represents NVIDIA at industry events, workshops, and conferences.

Build agentic engineering workflows that automate code generation, testing, tuning, and optimization across NVIDIA frameworks, compilers, and developer tools. Partner with engineering teams to identify high-impact use cases, develop multi-agent orchestration systems, integrate agents into Git and CI workflows, validate results on GPUs, and create scalable infrastructure and evaluation methods for broader adoption.

Develop and lead production software-defined networking for NVIDIA’s global GPU cloud infrastructure. Design multi-tenant control and data planes using Open vSwitch, OVN, OpenFlow, Kubernetes, C, and Go. Build networking services, plugins, automation, testing, observability, and reliability improvements. Lead architecture and code reviews, mentor engineers, support on-call operations, and resolve complex production incidents through diagnostics and source-level debugging.

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.