At a Glance
- Tasks: Lead the development of cutting-edge machine learning models and algorithms.
- Company: Innovative tech firm at the forefront of machine learning and hardware integration.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement opportunities.
- Why this job: Join a team pushing the boundaries of machine learning and make a real impact.
- Qualifications: Proven experience in developing novel algorithms and a strong mathematical background.
The predicted salary is between 60000 - 80000 £ per year.
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.
- Build 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-contrastive 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.
Machine Learning Researcher - London in Slough employer: microTECH Global LTD
Join a forward-thinking company in London that champions innovation and excellence in machine learning. With a collaborative work culture, we prioritise employee growth through continuous learning opportunities and cutting-edge projects that push the boundaries of technology. Our commitment to fostering a supportive environment ensures that you can thrive both personally and professionally while contributing to groundbreaking advancements in the field.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Researcher - London in Slough
✨Tip Number 1
Network like a pro! Attend meetups, conferences, or workshops related to machine learning. It's a great way to connect with industry experts and potential employers who might be looking for someone just like you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving novel algorithms or hardware-aware ML. This will give you an edge and demonstrate your hands-on expertise to potential employers.
✨Tip Number 3
Don’t just apply blindly! Tailor your approach by researching the company and its projects. When you reach out, mention specific aspects of their work that excite you and how your skills can contribute to their goals.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining us. Make sure to highlight your experience with state-of-the-art models and any relevant publications in your application.
We think you need these skills to ace Machine Learning Researcher - London in Slough
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Researcher role. Highlight your experience with novel algorithms and any relevant publications. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for machine learning and how your background makes you a perfect fit for our team. Don’t forget to mention any hands-on experience with hardware or unconventional training algorithms.
Showcase Your Projects:If you've worked on any interesting projects, especially those involving deep learning or hardware-aware ML, make sure to include them. We love seeing practical applications of your skills, so don’t hold back!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at microTECH Global LTD
✨Know Your Algorithms
Make sure you brush up on the latest machine learning algorithms and architectures. Be ready to discuss your previous work, especially any novel algorithms you've developed. If you've published papers in top conferences or journals, be prepared to explain your contributions and the impact they had.
✨Showcase Your Hardware Knowledge
Since this role involves hardware-aware ML, it’s crucial to demonstrate your understanding of FPGAs, ASICs, and other hardware components. Bring examples of how you've optimised models for specific hardware in the past, and be ready to discuss the challenges you faced and how you overcame them.
✨Collaborative Spirit
This position requires close collaboration with the photonics and hardware team. Prepare to talk about your experience working in interdisciplinary teams. Highlight any projects where you successfully collaborated with others to achieve a common goal, especially in a research setting.
✨Mathematics is Key
A deep understanding of mathematics is essential for this role. Brush up on concepts like latent space and intrinsic dimensionality. Be ready to solve problems on the spot or discuss how you’ve applied these mathematical principles in your previous work.
