Senior ML Compiler Engineer — Edge AI On-Device

Qualcomm

Santa Clara (CA)

On-site

USD 151,000 - 227,000

Full time

3 days ago
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Job summary

Qualcomm Technologies, Inc. is seeking a Machine Learning Engineer to create and optimize ML compiler technologies that translate PyTorch/ONNX models into efficient code for CPU, GPU, and NPU devices.

You will work with cross-functional teams to advance ML frameworks across Qualcomm platforms. The role requires strong Python and C++ skills, experience with ML compiler concepts, and familiarity with MLIR/ONNX/TVM stacks.

Qualifications

  • Bachelor's degree in CS/Engineering or related field with 2+ years of relevant work experience
  • Master's degree in CS/Engineering or related field with 1+ year of relevant work experience
  • PhD in CS/Engineering or related field
  • Proficient in Python and C++
  • Solid understanding of ML compiler concepts and experience with MLIR/ONNX/TVM

Responsibilities

  • Build & maintain machine learning compiler technologies that turn AI models into efficient device code
  • Contribute to AI hub compiler, ONNX Runtime QNN optimization and cross-backend compatibility
  • Build debugging tools to diagnose accuracy loss or slowdowns with clear diagnostics
  • Mentor teammates and provide technical guidance
  • Explain complex compiler ideas clearly to chip engineers and partners

Skills

Python
C++
Communication skills
CI/CD pipelines

Education

Bachelor's degree in CS/Engineering or related field
Master's degree in CS/Engineering or related field
PhD in CS/Engineering or related field

Tools

MLIR
ONNX
TVM
PyTorch
ONNXRuntime
LiteRT
ExecuTorch

Job description

Qualcomm Technologies, Inc. is seeking a Machine Learning Engineer to create and optimize ML compiler technologies that translate PyTorch/ONNX models into efficient code for CPU, GPU, and NPU devices.

You will work with cross-functional teams to advance ML frameworks across Qualcomm platforms. The role requires strong Python and C++ skills, experience with ML compiler concepts, and familiarity with MLIR/ONNX/TVM stacks.

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