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.
Are you the right candidate for this opportunity Make sure to read the full description below.
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. xwzovoh
- 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