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NVIDIA is seeking a Vehicle Integration Engineer for the Autonomous Vehicle testing fleet. This is a key role to support our AV software development. Together with OEM partners we are bringing safety to streets at a large scale. We are widely considered to be one of the technology world’s most desirable employers, with some of the most forward-thinking people in the world working with us. With our invention of the GPU, we set the stage for advances in the field of artificial intelligence, computer vision, and medical research. Today, we are developing groundbreaking products in robotics and intelligent autonomous machines. There has never been a more exciting time to join us!
What you will be doing:
Integrate a variety of subsystems into customer vehicle E/E architecture, iterate from development platform to production vehicle, adapt system to various carlines, and to different OEM brands.
Analyze complex technical issues across the stack, support function teams in debugging end-to-end function issues with deep understanding of related components. Ensure function success, and drive issues to closure.
Diagnosing software and hardware subsystems. Work with component team to root cause issues. Track and verify the fix.
Lead the team to build test vehicles, define criteria to sign-off vehicles to the test fleet. Test and track component firmware updates. Debug vehicle platform issues for fleet health and consistency.
What we need to see:
Bachelor's Degree or higher in Electronics/Electrical, Robotics, Mechatronics (or equivalent relevant experience)
5+ years of autonomous vehicle engineering experience (OEM/Tier1/StartUp).
Technically savvy, and motivated to learn new tech spanning cloud to car
Solid understanding of CAN, FlexRay, automotive Ethernet and various automotive sensors like Radar, Lidar, Cameras
Hands on experience working on hardware components, and retrofitting test vehicles
Problem solver and “Buck stops with you” leadership
Experience managing technical teams or tooling teams
Experience with Linux
Ways to stand out from the crowd:
Familiar with classical AutoSAR and adaptive AutoSAR
Prior experience in AV technical role
Experience with python/bash/C++
Background with QNX
