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Perform silicon and package qualification for gaming and datacenter GPUs, define and validate test programs with Test/DFx, drive failure analysis across teams, analyze data to identify improvements, and support qualification timelines and risk management.

Lead NVIDIA's PCB and PCBA capability: set multi-year strategy and roadmap, build a world-class PCB/SMT team, prioritize investments, align suppliers, drive supplier execution and root-cause closure, raise manufacturing reliability using SPC/CpK/CTQ and inline metrology, and partner with design, SI, and reliability to qualify technologies for volume production.

Develop, validate, and support manufacturing diagnostic test programs for SoC products. Collaborate with ASIC, ATE, validation, software/firmware, and production teams to diagnose complex hardware/software issues, automate processes, and drive test program and process improvements.

Lead technical engagement with OEM/ODM ecosystem partners to enable MGX-based high-performance hardware. Provide system-level electrical, mechanical, and thermal engineering expertise; drive integration of compute, networking, power, cooling, and mechanical subsystems; define validation strategies; lead product reviews and readiness assessments; deliver partner training and onboarding; collaborate across product, engineering, operations, manufacturing, and business teams to grow the MGX hardware ecosystem.

Lead architecture and development of Vehicle and Sensor Abstraction Layers (VAL/SAL) for autonomous vehicles. Manage teams building low-latency C++ systems with GPGPU (CUDA) acceleration, interfacing sensors and actuators, ensuring scalable integration, safety-compliant automotive software (MISRA), and validating via real-world and synthetic testing. Oversee in-vehicle tests, data analysis, unit testing, documentation, and cross-functional collaboration to deliver production-quality autonomy software.

Join an agile EDA software team to develop and automate PCB electrical design and layout tools (Cadence System Capture, Allegro) for system designers. Assess requirements, build tooling solutions, integrate databases and automation, and document processes to improve SI and design workflows.

Lead research and engineering to improve LLM inference algorithms and systems: analyze and optimize agentic LLM workloads for latency and throughput, design scalable datacenter systems, and collaborate across software, hardware, and external partners to integrate performance improvements.

Lead engineering and data teams to architect NVIDIA's data Inference Acceleration strategy, focusing on deep learning and performance improvements for Edge and Enterprise devices.

The role involves collaborating on AI GPU deployment, optimizing performance, mentoring engineers, and improving user experience on NVIDIA RTX platforms.

As a Senior Deep Learning Systems Engineer, you will optimize datacenters for AI applications by analyzing performance metrics and developing software tools.