Senior Software Engineer, Deep Learning Inference - Automotive Safety

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Software Engineer focused on developing high-performance deep learning inference software for safety-critical automotive applications using C++. The role involves integrating hardware functionalities into TensorRT, optimizing performance, and ensuring rigorous safety validation and documentation.

What you'd actually do

  1. Lead the design and development of high-performance deep learning inference software for safety-critical automotive applications using modern C++
  2. Orchestrate the integration of new hardware functionalities into TensorRT's compiler and runtime for specialized and constrained platforms
  3. Work closely with teams and stakeholders across the hardware and software stack to understand and leverage new technologies to improve TensorRT's functionality and performance
  4. Guide the design and implementation of robust, high-quality C++ code in alignment with Modern C++ standards and safety-critical software requirements
  5. Drive systematic development of test plans from unit to integration level, with emphasis on rigorous safety validation and verification

Skills

Required

  • Bachelors, Masters, PhD, or equivalent experience in relevant fields (Computer Engineering, Computer Science, Electrical Engineering, AI)
  • At least 5+ years of relevant software development experience
  • Strong C++ skills, including knowledge of and application of best practices with C++14 or newer standards
  • Familiarity with deep learning concepts and frameworks
  • Experience with safety-critical software development, including rigorous testing, validation, and documentation practices
  • A track record of taking initiative and driving projects to completion
  • Excellent interpersonal skills and a collaborative, pragmatic approach to solving problems

Nice to have

  • Experience with automotive safety standards (e.g., ISO 26262, ASIL) or other functional safety frameworks
  • Proficiency with Python and/or CUDA, ideally with experience in a professional environment
  • Background with systems programming, embedded systems, and/or compiler development
  • Experience in software performance benchmarking, profiling, and optimizations
  • Experience with state-of-the-art deep learning models (such as Large Language Models) and frameworks for inference

What the JD emphasized

  • safety-critical
  • rigorous safety validation and verification
  • safety-critical software requirements
  • safety-critical properties
  • automotive safety standards

Other signals

  • high-performance AI inference
  • safety-critical applications
  • automotive safety