Cuda Kernel Engineer Jobs in Michigan (NOW HIRING)

About the Mission

GM's vision of Zero Crashes, Zero Emissions, and Zero Congestion guides everything we do in autonomous and assisted driving. The AV organization is building advanced automated driving technologies,including Level 4-capable fully self-drivingsystems,tomove us toward safer, more sustainable, and more accessible mobility.

For theAIKernels & Compilers team, that mission shows up in the details: turning cuttingedgeperception, prediction, and planning research into productiongrade software that can run efficiently and reliably on real vehicles at scale. We pioneernew approachesto model export,kernel development, and performance engineering so that every cycle on our accelerators translates into better situational awareness, faster reaction times, and more robust behavior on the road.

If you want your compiler andkernelsworktodirectlyinfluencehow automated vehicles understand and react to the world - whileoperatingatthesafety,reliabilityand scaleof a company like GM - this is where that impact becomes real.

About the Team

TheAI Kernelsteam builds highperformance GPU kernels and custom libraries that sit at the heart ofouronvehicle ML inference forADAS and autonomous driving. We own making core AI workloads faster, more reliable, and easier tomaintainand deployon real cars, under realworld constraints.

That means:

• Designing and implementing custom operators when vendor libraries hit their limits

• Integrating those kernels deep into our ML runtime stack

• Debugging and tuning GPU performance across the AV software stack, often on hardwareintheloop

We partner closely withAI Solutions, AI Compilers, AI Architecture, and AI Toolingto ensure models deploy efficiently to the car while consistently meetingstrict latency, throughput, and reliability targets. If you enjoy pushing GPUs to their limits and seeing your work directlyimpacthow autonomous vehicles perceive and act in the world, this is the team for you.

Whatyou'llbe doing (Responsibilities)

• Design, implement, benchmark, and iterate on CUDA-based kernels and custom operators to squeezeevery lastdrop of performance out of on-vehicle inference workloads.

• Build and improve tooling and infrastructure that make it easier to profile, debug, andvalidateCUDA kernels and accelerator-backend code across the AV stack.

• Partner with AI Solutions, Compilers, and Architecture to translate model and system requirements into concrete kernel roadmaps, priorities, and project plans.

• Collaborate with cross-functional teams (compiler, performance tooling, runtime, deployment solutions) to deliver reusable, reliable, high-performance libraries into production.

• Maintain hightechnology standards, methodologies, processes, and guidelines for GPU kernel development and performance engineeringthrough code review.

• Manage relationships with internal customers to ensure our kernels and libraries meet real-worldneeds

Your Skills & Abilities (Required Qualifications)

• Minimum2+ years of relevant industry experience or equivalent experience

• BS, MS or PhD in CS, or related technical field

• Excellent GPU programming skills in CUDA, with a thorough understanding of parallel programming patterns and GPU architecture.

• Hands-on experience benchmarking, profiling,debuggingandoptimizingaccelerator libraries and kernels to extractoptimalperformanceusing theNSightsuite of tools or similar.

• Strong background in software architecture, librarydesign,and design patterns.

• Strong C++ programming skills with the ability to feel comfortable in large codebases.

• Solid background in system performance, high performancecomputingand/or architecture-aware optimizations.

• Strong communicationskills and the ability to work collaboratively within a team

• Excellent analytical and problem-solving skills

What Will Give YouACompetitive Edge (Preferred Qualifications)

• 2+ years of relevant industry experience or equivalent experience

• Experience withtensor core programming,CUTLASS and/orCuTe

• Experience withML model architectures,in particulartransformer-based

• Experience with low latencyorreal time systems

• Experience withlower levelsof an accelerator software stack (i.e.drivers, runtimes, andcompilers)