Software AI Model Optimisation & Benchmarking Engineer

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Software AI Model Optimisation & Benchmarking Engineer

Client:

IC Resources

Location:

United Kingdom

Job Category:

Other

EU work permit required:

Yes

Job Reference:

80fcb4be7958

Job Views:

2

Posted:

30.03.2025

Expiry Date:

14.05.2025

Job Description:

About the Role

We are seeking a Machine Learning / AI Engineer with expertise in model optimisation, acceleration, and deployment on AI accelerators (GPUs, NPUs, ONNX-compatible hardware). You will work on algorithm development, performance tuning, and benchmarking of a new AI accelerator to different AI applications (LLM's/ViT's/DiT's/CompVision). Knowledge of optical computing and photonics-based AI acceleration is highly beneficial.

Key Responsibilities

• Develop, optimise, and accelerate ML models using TensorFlow, PyTorch, and ONNX.
• Implement quantisation, pruning, and other optimisation techniques to 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 with CUDA, cuDNN, TensorRT, and other low-level libraries for AI acceleration.
• Explore and apply optical computing techniques for next-generation AI acceleration.
• Collaborate with software, hardware, and optical computing teams to ensure seamless deployment.

• Strong experience in Python with TensorFlow/PyTorch.
• Experience in AI model optimisation techniques (quantisation, pruning, knowledge distillation).
• Knowledge of AI accelerators (GPUs, NPUs, ONNX, TensorRT, OpenVINO).
• Hands-on experience with benchmarking tools and performance profiling.
• Understanding of parallel computing, memory optimisation, and hardware-aware ML development.
• Familiarity with optical computing concepts (photonics, optical neural networks) is a plus.

• Experience with low-level CUDA programming for deep learning.
• Familiarity with edge AI and deploying models on embedded systems.
• Exposure to optical-based AI acceleration and photonics-driven hardware.
• Experience with large-scale distributed training (e.g., Horovod, DeepSpeed).

Contact Jonny for more information.

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Created on 30/03/2025 by TN United Kingdom