As a Machine Learning Engineer, you will take the lead on optimising the functionality, performance, and algorithmic engineering powering our machine learning models. This is a critical role for a hands-on expert who can deliver state of the art models, set the standard for performance, and ensure our models are accurate, robust, efficient and scalable.

What you’ll do:

• Lead the development and refinement of novel machine learning architectures and algorithms harnessing our nonlinear dynamics. Building deeper network architectures that maximise efficiency and performance.

• Design, build and test models both on device and using in-house simulation framework.

• Collaborate closely with the wider photonics and hardware team to design and evaluate general metrics to assess the computational properties of the hardware and optimise for computational performance.

• Research state-of-the-art machine learning & machine vision techniques and adapt them to be compatible with our hardware.

• Experience:
• Proven track record of developing novel algorithms (papers in NeurIPS, ICML, ICLR, CVPR, or Nature/Science journals)
• Hardware Aware ML / Neuromorphic Computing:
  
  • FPGAs, ASICs, analog computing chips, spiking neural network (hardware), edge AI
• Unconventional training algorithms
  
  • Reservoir computing, self-constrastive learning, forward-forward learning, evolutionary algorithms, equilibrium propagation
• Physics informed neural networks, or applied ML to physics problems
• Deep understanding of mathematics, algebra / topology - key words are latent space, intrinsic dimensionality
• Experience working with hardware as well is a bonus