Staff Machine Learning Engineer - AI/ML Compiler

Compiler Pipeline & Infrastructure Build and maintain ONNX-based compilation paths using ONNX IR: graph transformation passes, op validation, and opset compatibility handling Build and maintain PyTorch compilation paths consuming torch.export output, including dynamic shapes, custom ops, and ATen IR decomposition Contribute to ONNXRuntime QNN execution provider: graph optimizations, graph partitioning, and op validation and lowerings Collaborate with QAIRT and QNN teams to ensure correct and efficient model execution across CPU, GPU, and NPU backends Build tooling to analyze, profile, and debug compilation failures, accuracy regressions, and performance degradations; develop clear, actionable developer-facing diagnostics Works independently on open-ended compiler and infrastructure challenges Provides technical guidance and mentorship to team members Communicates complex compiler and runtime concepts to varied audiences: SoC engineers, BU partners, and external ML developers Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

OR PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

3+ years of industry experience in ML infrastructure, compiler engineering, or AI framework development Proficient in Python and C++ Solid understanding of ML compiler concepts (graph IRs, operator fusion, shape inference, lowering passes, backend partitioning) and hands-on experience with one or more compiler stacks such as MLIR, ONNX, or TVM Experience with PyTorch model export (torch.export, torch.compile, FX, ATen IR) and on-device deployment frameworks such as LiteRT, ExecuTorch, or ONNXRuntime Familiarity with SoC-level constraints (memory bandwidth, compute precision, NPU/DSP execution) and hardware-specific runtimes such as QAIRT/QNN is a plus Experience building automated CI/CD pipelines for model compilation and validation at scale Strong written and verbal communication skills; proficiency with git and software engineering best practices