The candidate should meet the following requirements
Job Description
Role Description
The ideal candidate will have
About the role:
Join a specialist machine learning team working at the intersection of deep learning, model optimisation, and efficient deployment. You will help build and deploy advanced ML models for low-latency speech recognition and foundation LLMs, focusing on reducing power consumption while maximising performance.
Your work will include:
1. Training state-of-the-art models on production-scale datasets.
2. Compressing and optimising models for accelerated inference on modern hardware.
3. Researching and implementing innovative ML techniques tailored for efficient deployment.
4. Deploying and maintaining customer-facing training libraries.
Your initial focus will be on speech recognition models, where you will:
5. Optimise training workflows for multi-GPU environments.
6. Manage and execute large-scale training runs.
7. Tune hyperparameters to improve both inference quality and performance.
What you’ll be working on This is an end-to-end optimisation role, from algorithms through to deployment on modern silicon, with a mission to enable high-performance, low-power AI in production environments. You will work on deep technical challenges alongside engineers and researchers who care about efficiency, precision, and impact.
What they're looking for:
8. Strong practical experience in training deep learning models at scale.
9. Knowledge of optimising ML workflows for multi-GPU environments.
10. Experience with model compression, quantisation, and deployment for low-latency applications.
11. Familiarity with frameworks such as PyTorch, TensorFlow, or similar.
12. Ability to tune models for real-world performance constraints.
13. A collaborative mindset, able to contribute ideas and adapt to feedback in a small, high-trust team environment.
Why join?
14. Work on meaningful projects that contribute to reducing the energy footprint of global AI workloads.
15. Collaborate in a friendly, multi-disciplinary team that values technical excellence, innovation, and open discussion.
16. Develop your skills by working on cutting-edge optimisation challenges with a clear path from research to deployment.
17. Enjoy a collaborative on-site culture with shared meals, games, and a supportive team environment, while retaining flexibility for hybrid working.