Machine Learning Engineer | Cambridge | Consulting

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:

Training state-of-the-art models on production-scale datasets.
Compressing and optimising models for accelerated inference on modern hardware.
Researching and implementing innovative ML techniques tailored for efficient deployment.
Deploying and maintaining customer-facing training libraries.Your initial focus will be on speech recognition models, where you will:

Optimise training workflows for multi-GPU environments.
Manage and execute large-scale training runs.
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:
Strong practical experience in training deep learning models at scale.
Knowledge of optimising ML workflows for multi-GPU environments.
Experience with model compression, quantisation, and deployment for low-latency applications.
Familiarity with frameworks such as PyTorch, TensorFlow, or similar.
Ability to tune models for real-world performance constraints.
A collaborative mindset, able to contribute ideas and adapt to feedback in a small, high-trust team environment.Why join?
Work on meaningful projects that contribute to reducing the energy footprint of global AI workloads.
Collaborate in a friendly, multi-disciplinary team that values technical excellence, innovation, and open discussion.
Develop your skills by working on cutting-edge optimisation challenges with a clear path from research to deployment.
Enjoy a collaborative on-site culture with shared meals, games, and a supportive team environment, while retaining flexibility for hybrid working