Senior ML Compiler Engineer
Job Summary
As a Senior Compiler Engineer on the AI Kernels & Compilers team, you will develop the compilation stack that transforms high-level models into optimized inference artifacts. You will focus on building robust model export pipelines and compiler transforms to improve on-vehicle latency and model fidelity. This role involves close collaboration with hardware and runtime teams to ensure efficient execution on autonomous driving platforms.
Responsibilities
- Build/evolve model compilation toolchain
- Architect compiler passes for latency/memory optimization
- Collaborate on hardware/software co-design
- Establish standards for model validation and debugging
Required Skills
- 3+ years Compiler experience
- Proficiency in Python/C++
- ML Framework expertise (PyTorch, TensorFlow, etc.)
- Deep understanding of MLIR, ONNX, or TensorRT
Job Details
### Responsibilities
- 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.
### Required Qualifications
- 3+ years of experience in the field of compilers.
- Experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX) and software stack (e.g., ONNX, MLIR, XLA, TVM, TensorRT, etc).
- Expertise in writing production quality Python/C++ code.
- Expertise in the software development life-cycle - coding, debugging, optimization, testing, integration.
- BS, or higher degree, in CS/CE/EE, or equivalent.
### Preferred Qualifications
- Experience building and optimizing ONNX-based model export and deployment pipelines.
- GPU programming (CUDA) and familiarity with ML SW stack (e.g., cuDNN, cuBLAS).
- Experience with ML accelerators and hardware architecture.
- Experience developing and deploying machine learning models.