• Design, develop, and deploy scalable components across the entire stack, from cloud infrastructure to the user interface. • Champion and enforce modern software practices, focusing on quality, cost efficiency, and observability throughout the development lifecycle (e.g., thorough testing, performance monitoring, logging, and infrastructure-as-code principles). • Work with technologies to model, store, and query complex, interconnected data structures, ensuring optimal performance for analytical workflows. • Lead the frontend development, focusing on advanced data visualization and intuitive user interaction to enable effective "data walking" and key insight discovery. • Design and implement features that promote collaboration, knowledge sharing, and connecting users with their peers across the platform.

• Address customer and partner optimization challenges by engaging directly with automotive OEMs and robotics associates to analyze, debug, and improve deep learning models on NVIDIA platforms • Own performance benchmarking by driving efforts to achieve leading results on MLPerf Edge and industry benchmarks, defining methodology and ensuring reproducibility • Evaluate emerging model architectures by analyzing DL architectures, including vision encoders, multi-modal VLMs, for compilation feasibility, memory footprint, and latency on target SOCs • Collaborate across teams by partnering with compiler, runtime, and hardware teams to connect model-level insight with platform capabilities • Deliver TensorRT and compiler-stack solutions for edge by creating and deploying inference solutions on Jetson, DRIVE, and GPU + ARM platforms for AV and robotics workloads. • Develop Proofs of Readiness (PORs) and work closely with compiler team on Torch-TRT, MLIR-TRT, and related frameworks to bridge performance gaps.

• Drive CI and infrastructure capabilities that make deep learning compiler development fast, reliable, and scalable. • This includes improving signal-to-noise (flake reduction, reproducibility, and richer diagnostics), accelerating iteration cycles, scaling capacity and coverage across models/hardware/software configurations, and building strong observability (metrics, logging, tracing, dashboards) so failures are easy to understand and fix. • Explore practical uses of AI to enhance CI workflows—such as smarter test selection, automated triage/summarization, and faster issue isolation—ultimately increasing the quality and speed of deep learning compiler development, testing, and release.

• Architect, build, and scale services and infrastructure components • Own critical systems including Hadoop, Presto clusters, Kubernetes infrastructure, and deployment pipelines • Develop and deploy services to improve availability, usability, and observability • Write, review, and debug production-grade code in Python and Java • Design and maintain high-availability, fault-tolerant systems • Implement observability solutions (metrics, logging, alerting) • Contribute to infrastructure security and configuration management best practices • Support engineering teams with platform enablement and troubleshooting • Contribute to open-source projects when required