Qualcomm
AI

Machine Learning Engineer - PyTorch/C++ Development (NPU Architecture Team)

Qualcomm · Cork, C, IE

Actively hiring Posted 3 months ago

Responsibilities

  • Develop and enhance ML compilers using PyTorch and C++ tailored for Qualcomm’s Neural Processing Unit (NPU) architecture.
  • Design and implement advanced quantization techniques to improve model efficiency and accuracy.
  • Optimize ML workloads for deployment across diverse devices, ensuring top-tier performance and energy efficiency.
  • Work directly with strategic customers to address specific challenges and deliver tailored ML solutions.
  • Collaborate with architecture and software engineering teams to integrate cutting-edge ML technologies into Qualcomm platforms.
  • Research, prototype, and implement innovative solutions in ML compiler design and system-level optimizations.
  • Evaluate and debug ML performance to identify and resolve bottlenecks in complex workflows.
  • Create thorough technical documentation to share knowledge and advancements with the team. Qualifications:
  • Qualifications:
  • Bachelor’s or advanced degree in Computer Science, Electrical Engineering, Machine Learning, or a related discipline.
  • Strong expertise in PyTorch and C++ programming.
  • Experience with ML workload analysis, compiler development, and quantization techniques.
  • Familiarity with deep learning frameworks such as TensorFlow or ONNX is a plus.
  • Proven track record of solving complex performance and efficiency challenges in hardware-aware ML solutions.
  • Ability to work collaboratively with strategic customers and deliver impactful results.
  • Excellent analytical skills and ability to thrive in a high-performance team environment.

Basic qualifications

  • Bachelor's degree in Science, Engineering, or related field and 2+ years of ASIC design, verification, validation, integration, or related work experience.

Preferred qualifications

  • Experience with ML model deployment on hardware accelerators such as GPUs, TPUs, or NPUs.
  • Understanding of system-level architecture and low-level programming.
  • Contributions to ML research or publications in relevant fields are an advantage.

Tags & focus areas

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Machine Learning Pytorch Ai