Research Engineer — Applied Science (ML)

Full-time

Location: London

Department: Research & Engineering

Bridge the gap between cutting-edge AI research and practical application. You will own the design, training, and fine-tuning of advanced machine learning models to solve complex, domain-specific problems. We value engineering driven by first-principles thinking — someone who genuinely enjoys digging into the math of the latest arXiv papers, exploring novel architectures, and rapidly translating theoretical breakthroughs into functional, high-performance prototypes.

What you'll do

• Model architecture & design: research, design, and prototype deep learning architectures tailored to our data domain — generative models, geometric deep learning, large-scale transformers.
• Experimental exploration: own rigorous experimental roadmaps — ablation studies, hyperparameter optimisation, and loss-function engineering.
• Algorithmic & compute optimisation: integrate novel optimisations (custom attention, tokenisation, layer normalisation) with an eye on memory efficiency and training stability.
• Data strategy & feature engineering: design representation, augmentation, and synthesis strategies for complex, constrained biological datasets.
• Collaborative hand-off: partner with MLOps so prototypes are built for stability and transition cleanly into scaled production.