Senior Desktop Software Developer leading a greenfield rewrite of Sync.com desktop application with a modern Rust - based architecture. Focus on technical ownership and high - quality software delivery.
About the role
- Senior Engineer optimizing deep learning inference on edge hardware for autonomous vehicles and robotics at NVIDIA. Collaborating with automotive OEMs and addressing complex optimization challenges.
Responsibilities
- 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.
Requirements
- Master’s degree or equivalent experience in Computer Science, Electrical Engineering, or a related field
- 12 + years of industry experience with over 8 years in deep learning model optimization, inference engineering, or neural network compilation
- Adept at interpreting and reasoning about model architectures at the operator/kernel level
- Over 5 years of validated expertise in embedded/edge software, experience delivering production inference solutions within power-limited, latency-sensitive deployment environments
- Deep knowledge of current DL architectures: transformers, attention variants, vision encoders (ViT), multi-modal/vision-language model frameworks, and experience with diffusion models and/or state space models
- Expert knowledge of GPU architecture fundamentals, CUDA, and low-level performance optimization using heterogeneous computing
- Experience with TensorRT, compiler IRs, or equivalent inference optimization toolchains
- Solid understanding of embedded operating system internals (QNX/Linux), memory management, C/C++, and embedded/system software concepts
- Background in parallel programming (e.g., CUDA, OpenMP) and experience reasoning about memory hierarchies, data movement, and compute utilization
- Demonstrated capability to collaborate directly with external partners and customers in a deep technical role, solving their workload issues, identifying performance problems, and providing solutions within production limitations.
Benefits
- Eligible for equity and benefits
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