Senior Systems Software Engineer - Deep Learning Solutions
$224k - $357k • Remote • US, CA, Santa Clara
Posted 5d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
NVIDIA is seeking a Senior Systems Software Engineer to join their Deep Learning Solutions team. This role focuses on optimizing deep learning inference for autonomous vehicles and robotics on edge devices. The ideal candidate will be a hands-on specialist capable of analyzing model architectures at the operator level, identifying performance bottlenecks through kernel trace analysis, and evaluating modern AI architectures on GPU and SOC platforms. This position directly contributes to enhancing real-time perception and response capabilities for autonomous systems, working at the intersection of model frameworks, compiler technology, and embedded hardware. Collaboration with automotive OEMs, robotics teams, and internal hardware groups is a key aspect of this role.
Responsibilities
- Address customer and partner optimization challenges by analyzing, debugging, and improving deep learning models on NVIDIA platforms.
- Drive performance benchmarking efforts for MLPerf Edge and industry benchmarks, defining methodologies and identifying optimization priorities.
- Evaluate emerging DL architectures for compilation feasibility, memory footprint, and latency on target SOCs.
- Collaborate with compiler, runtime, and hardware groups to align model-level insights with platform capabilities.
- Contribute to build reviews and internal roadmap development based on customer workload patterns.
- Represent NVIDIA externally by sharing deep learning optimization expertise at conferences and events.
- Deliver and deploy TensorRT and compiler-stack inference solutions for edge platforms like Jetson and DRIVE.
- Develop Proofs of Readiness (PORs) and collaborate on frameworks like Torch-TRT and MLIR-TRT to improve performance.
Requirements
- Master’s degree or equivalent experience in Computer Science, Electrical Engineering, or a related field.
- Over 12 years of industry experience, with at least 8 years specializing in deep learning model optimization, inference engineering, or neural network compilation.
- Proficiency in understanding and reviewing model architectures at the operator/kernel level.
- Over 5 years of validated expertise in embedded/edge software, delivering production inference solutions in power-limited, latency-sensitive environments.
- Comprehensive knowledge of contemporary DL architectures (transformers, attention variants, ViT, VLMs, diffusion models, 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 OS internals (QNX/Linux), memory management, C/C++, and embedded/system software concepts.
- Background in parallel programming (e.g., CUDA, OpenMP) and 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 workload issues and providing solutions.
Benefits
- Equity
- Benefits