Senior Software Performance Engineer - AV Platform

We are now looking for a Senior Software Performance Engineer for Autonomous Vehicles! Our team builds NVIDIA's end-to-end autonomous driving applications. We are seeking senior software engineers who are passionate about performance with an interest in optimizing self-driving solutions that run on NVIDIA's multi-computer and heterogenous HW architectures.

What you'll be doing:

• Develop, maintain and optimize latency and throughput of NVIDIA's L2/L3/L4 autonomous driving solutions.
• Devise acceleration strategies and patterns to improve software architecture and its efficiency on our computers with multiple heterogeneous hardware engines while meeting or exceeding product goals.
• Develop highly efficient product code in C++, making use of algorithmic parallelism offered by GPGPU programming (CUDA)/ARM NEON while following quality and safety standards such as defined by MISRA.
• Collaborate with HW, product, OS, and safety teams to design next-gen products.

What we need to see:

• MS or PhD degree in Computer Science, Computer Architecture, Electrical Engineering or related field (or equivalent experience).
• 12+ years of relevant professional experience working on autonomous vehicles software.
• Excellent C and C++ programming skills.
• Solid understanding of programming and debugging techniques, especially for parallel architectures.
• Good understanding of system software/operating systems and computer architecture.
• Experience with performance analysis, optimizations and benchmarking.
• Outstanding communication and collaboration skills as this role might require significant interfacing with other teams within NVIDIA.

Ways to stand out from the crowd:

• Understanding of embedded architectures and real-time operating systems & scheduling.
• Strong mathematical fundamentals, including linear algebra and numerical methods.
• Experience implementing algorithms in robotics, computer vision, and/or machine learning.
• Software development experience with CUDA/GPGPU or any data parallel architectures.
• Deep learning architecture/performance work on any HW accelerator, especially if on GPUs.