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Staff Machine Learning Engineer – Datacentre AI (Riyadh)

Qualcomm · Riyad

Senior 🇬🇧 English
C++ Python Linux Deep Learning PyTorch JIT CUDA cuDNN Triton ExecuTorch Inductor TorchDynamo Qualcomm AI 100

Job description

About the role

Qualcomm is looking for a Staff‑level Machine Learning Engineer to drive AI development for its data‑centre infrastructure in Riyadh. The role requires deep hands‑on experience with C++/Python AI development on Linux, high autonomy, and the ability to lead and mentor engineering teams while staying execution‑focused.

Key responsibilities

  • Improve and optimise key deep‑learning models on Qualcomm AI 100.
  • Develop deep‑learning framework extensions for Qualcomm AI 100 in upstream open‑source repositories.
  • Collaborate with internal teams to analyse and optimise training and inference pipelines.
  • Build software tools and an ecosystem around the AI software stack.
  • Work on Triton, ExecuTorch, Inductor and TorchDynamo to create abstraction layers for inference accelerators.
  • Optimise workloads for scale‑up (multi‑SoC) and scale‑out (multi‑card) systems.
  • Integrate graph compiler optimisation throughout the deep‑learning pipeline.
  • Apply software‑engineering best practices throughout development.

Required profile

  • 8+ years of software engineering or related experience.
  • Proven ability to work independently, define requirements and lead development efforts.
  • Experience mentoring engineers through influence and example.
  • Strong problem‑solving mindset with a research‑oriented approach.
  • Bachelor’s, Master’s or PhD in Computer Science, Engineering, Machine Learning or a related field.

Required skills

  • C++ and Python programming.
  • Linux development environment.
  • Deep‑learning expertise (LLMs, NLP, vision, audio, recommendation systems).
  • Proficiency with PyTorch and JIT compilation.
  • Open‑source development practices.
  • CUDA and cuDNN.
  • Experience with Triton, ExecuTorch, Inductor, TorchDynamo.
  • Knowledge of Qualcomm AI 100 hardware and multi‑SoC/multi‑card scaling.

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Published 4 months ago

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Qualcomm

Riyad