Senior ML Compiler Engineer

Job Description

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‑driving systems, to move us toward safer, more sustainable, and more accessible mobility.

For the AI Kernels & Compilers team, that mission shows up in the details: turning cutting‑edge perception, prediction, and planning research into production‑grade software that can run efficiently and reliably on real vehicles  pioneer new approaches to 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 and kernels work to directly influence how automated vehicles understand and react to the world — while operating at the safety, reliability and scale of a company like GM — this is where that impact becomes real.

The AI Compiler team sits at the heart of how advanced AI models make it onto the car. We own the compiler that turns high‑level models into fast, reliable inference across GPUs powering GM’s next‑generation autonomous and assisted driving features.

Our work spans graph lowering, operator coverage, kernel integration, and deployment tooling, with a mandate to squeeze every millisecond out of on‑vehicle workloads while preserving correctness and robustness in real‑world conditions. We partner closely with AI Deployments, AI Solutions, Runtime, and AI Kernels teams to co‑design a platform that enables new ideas in research to be quickly and safely shipped to production fleets.

You’ll join a group of deep compiler, systems, and GPU engineers who enjoy working on hard problems, and diving into MLIR/ONNX and CUDA/Tensor

RT internals. We value clear thinking, strong engineering fundamentals, and a culture where people can do the best work of their careers on problems that directly shape the future of automated driving.

As a Senior Compiler Engineer on the AI Kernels & Compilers team, you will work on the compilation stack that takes high‑level models and turns them into highly optimized inference artifacts running on GM’s autonomous and assisted driving platforms.

You’ll be a key contributor to a pipeline and tooling that makes that path fast, reliable, and effortless for ML engineers across the AV organization to compile their models.

You will work on an evolving state‑of‑the‑art model export and compilation pipeline—from capturing high‑level model graphs, through intermediate representations and compiler transforms, into accelerator‑specific inference engines and their integration with our runtime—so that we can simultaneously optimize compilation throughput, model fidelity, and on‑vehicle latency. Along the way, you’ll build robust to validate numerical correctness, detect and bisect performance regressions, and surface clear, actionable diagnostics back to model authors.

If you want to work at the intersection of compilers, performance engineering, and real‑world autonomy, this role puts your decisions directly on the critical path of what runs on the car.

• Build and evolve the model compilation toolchain used to deploy large‑scale perception, prediction, and planning models to the AV.
• Architect new compiler passes and analysis that improve build times, memory footprint, and runtime latency while preserving—or intentionally trading off—fidelity under strict safety and reliability constraints.
• Collaborate closely with kernels, runtime, and hardware teams to co‑design interfaces, shape accelerator capabilities, and ensure the compiler exposes the right abstractions to unlock peak performance on each platform.
• Set standards and best practices for model export, validation, and debugging so that AV teams can iterate quickly with clear, reproducible performance and accuracy characteristics.

• 3+ years of experience in the field of compilers.
• Experience with ML frameworks (e.g.,…