Senior ML Accelerator Engineer GPU

About the Mission

GMs 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 technologiesincluding Level 4capable fully self-drivingsystemstomove 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 exportkernel 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 whileoperatingatthesafetyreliabilityand 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.

Whatyoullbe 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)

• Minimum3 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 profilingdebuggingandoptimizingaccelerator libraries and kernels to extractoptimalperformanceusing theNSightsuite of tools or similar.

• Strong background in software architecture librarydesignand 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)

• Experience withtensor core programmingCUTLASS and/orCuTe

• Experience withML model architecturesin particulartransformer-based

• Experience with low latencyorreal time systems

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