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As a Senior AI Developer Technology Engineer at NVIDIA, you'll design and optimize AI and HPC workloads, solve performance issues, and influence future hardware and software design.

The role involves researching and developing GPU-accelerated AI workloads, optimizing algorithms, and collaborating with technical experts to improve hardware performance.

Lead a team to optimize algorithms for Deep Learning and Machine Learning, define technical initiatives, and collaborate with various teams for hardware and software innovation.

Design, develop, and optimize a low-latency, high-quality media streaming stack for cloud gaming. Analyze GPU/CPU video pipelines, isolate bottlenecks, collaborate with hardware teams, build tools to measure video quality, and implement adaptive streaming and codec-based improvements for resilient performance under variable networks.

Lead strategic GTM and co-selling initiatives with Google Cloud to drive NVIDIA AI adoption. Develop and maintain technical enablement assets, run developer activations and events, collaborate with Google Cloud field and engineering teams, convert samples into demos and labs, and translate customer feedback into product and roadmap insights.

Lead validation of ML datacenter stacks from cluster to rack scale for CSPs: define test strategies, reproduce and triage customer bugs, validate fixes, run perf benchmarks, manage test data and tooling, produce release-readiness reports, and collaborate with internal and partner engineering teams.

Design, develop, and debug system software for NVIDIA Halos across QNX, Linux, and hypervisor environments. Build BSPs, I/O virtualization, drivers, boot and diagnostics for mixed-criticality NVIDIA SoC platforms. Apply formal methods and safety standards (ISO 26262/21434), optimize platform bring-up, and implement debugging, tracing, logging, and performance-analysis solutions. Collaborate with ASIC, firmware, OS, safety, security, and platform teams.

Design and validate Ethernet networking fabrics for AI/HPC clusters; mentor customers; build demos/POCs; create reference architectures and training; collaborate with engineering, product, and sales to secure design wins and drive product feature development.

Lead product marketing for NVIDIACUDA and core acceleration libraries. Own positioning, messaging, and go-to-market for libraries and AI frameworks, translate technical features into developer-focused narratives, and build AI-driven PMM automation and assets.

Design and research next-generation data-center networking solutions spanning ASIC design, algorithms, topologies, routing protocols, and systems. Collaborate with internal teams, partners, standards bodies, and communities to develop networking technologies for AI and accelerated computing.