Senior Edge DL Inference Engineer for Real-Time Autonomy

NVIDIA Corporation; Santa Clara (CA); On-site; USD 224,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is hiring a Senior Systems Software Engineer to optimize deep learning inference for edge devices in autonomous vehicles and robotics. You will examine model architectures at the operator level and identify performance improvements on Jetson, DRIVE, and GPU + ARM platforms.

Collaborate with compiler, runtime, and hardware teams to link model insight with platform capabilities, contribute to build reviews, and help shape internal roadmaps.

Qualifications

• Master’s degree or equivalent experience in Computer Science, Electrical Engineering, or a related field.
• 12+ years in the industry with at least 8 years in deep learning model optimization, inference engineering, or neural network compilation.
• 5+ years of validated expertise in embedded/edge software with production inference experience.

Responsibilities

• Address customer and partner optimization challenges: analyze, debug, and improve deep learning models on NVIDIA platforms for automotive and robotics.
• Own performance benchmarking: define methodology, reproduce results on MLPerf Edge and industry benchmarks, and translate results into optimization priorities.
• Evaluate emerging model architectures for compilation feasibility, memory footprint, and latency on target SOCs.
• Collaborate across teams with compiler, runtime, and hardware groups to align model insights with platform capabilities.
• Contribute to build reviews and internal roadmap priorities based on customer workload patterns.
• Represent NVIDIA externally by sharing optimization expertise at conferences and partner events.

Job description

NVIDIA is hiring a Senior Systems Software Engineer to optimize deep learning inference for edge devices in autonomous vehicles and robotics. You will examine model architectures at the operator level and identify performance improvements on Jetson, DRIVE, and GPU + ARM platforms.

Collaborate with compiler, runtime, and hardware teams to link model insight with platform capabilities, contribute to build reviews, and help shape internal roadmaps.