Software AI Model Optimisation & Benchmarking Engineer

IC Resources
1 month ago
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Machine Learning Scientists and Engineers: AI for Quantum

Machine Learning Scientists and Engineers: AI for Quantum

Machine Learning Scientists and Engineers: AI for Quantum

Photonics Engineer - 249

Photonics Engineer - 249

Lead Photonics Scientist

About the Role

We are seeking aMachine Learning / AI Engineerwith expertise inmodel optimisation, acceleration, and deployment on AI accelerators(GPUs, NPUs, ONNX-compatible hardware). You will work onalgorithm development, performance tuning, and benchmarkingof a new AI accelerator to different AI applications (LLM's/ViT's/DiT's/CompVision). Knowledge ofoptical computing and photonics-based AI accelerationis highly beneficial.

Key Responsibilities

  • Develop, optimise, and accelerate ML models usingTensorFlow, PyTorch, and ONNX.
  • Implementquantisation, pruning, and other optimisation techniquesto improve model efficiency.
  • Benchmark AI models on various accelerators (GPUs, NPUs, TPUs, optical computing platforms) and fine-tune performance.
  • Optimise inference and training pipelines for speed, energy efficiency, and hardware compatibility.
  • Work withCUDA, cuDNN, TensorRT, and other low-level libraries for AI acceleration.
  • Explore and applyoptical computing techniquesfor next-generation AI acceleration.
  • Collaborate with software, hardware, and optical computing teams to ensure seamless deployment.

Requirements

  • Strong experience inPythonwithTensorFlow/PyTorch.
  • Experience inAI model optimisation techniques(quantisation, pruning, knowledge distillation).
  • Knowledge ofAI accelerators(GPUs, NPUs, ONNX, TensorRT, OpenVINO).
  • Hands-on experience withbenchmarking toolsand performance profiling.
  • Understanding of parallel computing, memory optimisation, and hardware-aware ML development.
  • Familiarity withoptical computing concepts(photonics, optical neural networks) is a plus.

Nice to Have

  • Experience withlow-level CUDA programmingfor deep learning.
  • Familiarity withedge AIand deploying models on embedded systems.
  • Exposure tooptical-based AI accelerationand photonics-driven hardware.
  • Experience with large-scale distributed training (e.g., Horovod, DeepSpeed).

Contact Jonny for more information.

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