Nvidia Semiconductor Safety Engineer - New College Grad 

United States, Texas 

392997477

At NVIDIA, we are building the system, hardware, and software technology which enables autonomous driving. Our safety engineering team is currently looking for a Semiconductor Safety Engineer to help develop the next generation of system on chip (SoC) products for autonomous driving. On our SoC development team, you will have a chance to create and work with some of the most exciting technologies in the world, inventing autonomous driving products using camera systems, RADAR, LIDAR, HD maps, path-planning, and vehicle dynamics functionalities. You will get to work on technologies that support machine learning, computer vision, and robotics approaches. All these technologies are fundamental to develop an end-to-end AI computing product for self-driving-cars.

What you’ll be doing:

• Work closely with internal and partner safety engineering teams to deliver innovative products

• Collaborate with architects and designers to find optimal implementations of safety diagnostics in hardware and software

• Perform safety analysis and verification activities to confirm NVIDIA designs meet applicable performance targets, and aggressively work to improve products when issues are identified

• Work on the optimization, automation, and training of engineering teams and development of standard methodologies to ensure speed of light execution

• Provide support of third-party assessment of NVIDIA safety concepts and SoC implementations

• Assist in creation of safety manuals, analysis reports, and user guides

What we need to see:

• Bachelor’s degree or higher in an engineering field (e.g. computer science, electrical engineering, electronic systems engineering, aerospace engineering) or equivalent experience

• Semiconductor architecture understanding

• Understanding of functional safety development processes needed to follow the ISO 26262 functional safety standard

Ways to stand out from the crowd:

• Python scripting or other languages for data analysis/automation

• Understanding of neural networks, GPUs, and related design concepts

• Semiconductor design, systems engineering, or structured documentation (e.g. DITA, DocBook) experience