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Software Engineer, Autonomous Vehicles Software Systems - Early Career
General Motors
Sunnyvale, USA
Full-time
Mid
USD 99400-153900 / year
Discovered 2 weeks ago
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Hybrid: This role is categorized as Hybrid/Remote.
MI & CA Role: As a Software Engineer on the Software Validation team within the AV organization, you will play a critical role in the development of methods, tools and processes for the validation of autonomous vehicle behavior.
You will leverage your experience in software engineering to convert validation strategies into well-architected and implemented code that analyzes AV behavior.
Creating methods an tools to execute comprehensive, repeatable and precise validation for autonomy software and hardware.
You will work with a team of engineers to define best practices, raise the bar internally on coding quality, and verify the safety and performance of autonomous systems.
You will be responsible for shaping the future of evaluation methodologies for AI systems and other ADAS features, architecting solutions that meet the testing needs of AI developers, systems engineers, and safety stakeholders.
About the Organization: The Autonomous Vehicle (AV) organization is dedicated to advancing the development of autonomous vehicles through cutting-edge simulation technologies and novel iterative development processes.
The Behavior Validation team focuses on unlocking software launches and continuous release decisions via simulation verification and validation strategies, prototypes, and protocols.
Our collaborative environment fosters innovation and excellence, allowing us to push the boundaries of what is possible in autonomous vehicle testing.
What You’ll Do
Develop and prototype strategies to benchmark autonomy systems’ performance.
Convert validation strategies into production code that executes high quality AV behavior analysis for continuous and scaled software release cycles
Utilize performance data gathered from in-simulation, on-bench, on-road testing, to create actionable insights for AI developers to continuously improve the stack performance
Use data analytics to facilitate data driven design, validation, risk analysis, and prioritization decisions
Build tools that scale systems engineering prototyped testing products to additional users like the software release team and Embodied AI
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