Senior Systems Software Engineer - Deep Learning Solutions

Responsibilities

• Address customer and partner optimization challenges: Engage directly with prominent automotive OEMs and robotics associates to analyze, debug, and improve their deep learning models on NVIDIA platforms. We emphasize delivering solutions rather than just recommendations.
• Own performance benchmarking
• Drive efforts to achieve leading results on MLPerf Edge and industry benchmarks, as well as closed-source engagements with key partners. Define methodology, ensure reproducibility, and turn results into actionable optimization priorities.
• Evaluate emerging model architectures: Investigate new DL architectures, including vision encoders, multi-modal VLMs, hybrid SSM-Transformer backbones, diffusion/flow matching decoders, and multi-camera tokenizers, regarding compilation feasibility, memory footprint, and latency on target SOCs.
• Collaborate across teams: Work alongside our compiler, runtime, and hardware groups to link model-level insight with platform capabilities.
• Contribute to build reviews and help develop internal roadmap priorities based on real customer workload patterns.
• Represent NVIDIA externally: Share our deep learning optimization expertise at conferences, webinars, and partner events. Help elevate the broader team by bringing back insights and establishing guidelines.
• Deliver TensorRT and compiler-stack solutions for edge
• Build and deploy inference solutions on Jetson, DRIVE, and GPU + ARM platforms for AV and robotics workloads.
• Develop Proofs of Readiness (PORs) and collaborate closely with our compiler team on Torch-TRT, MLIR-TRT, and related frameworks to bridge performance gaps.

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