Senior Software Performance Engineer - Autonomous Vehicles

About the role

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

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.

- 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.

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