Machine Learning Scientist – Sequence Modelling

About the role

The Machine Learning Scientist will join the Rosalind team to design and apply advanced machine learning techniques to DNA and genetic data. The role focuses on uncovering associations between genetic variants and disease, supporting therapeutic discovery at Relation, an end‑to‑end biotech company in London.

Key responsibilities

• Develop and apply sequence‑modelling machine learning techniques to DNA sequences.
• Train, fine‑tune and evaluate DNA sequence models for variant interpretation, gene discovery and regulatory modelling.
• Collaborate with computational and experimental scientists to generate and validate ML‑driven hypotheses.
• Leverage large‑scale external and internal datasets to build disease‑focused models.
• Design robust evaluations to measure model quality, biological relevance and translational value.

Required profile

• PhD in machine learning, computational biology or related field, or equivalent industrial experience.
• Demonstrated experience applying machine learning to biological sequences or text.
• Flexibility to tackle new challenges at the intersection of biology and machine learning.
• Experience with transformers and their biomedical applications is desirable.
• Knowledge of lab‑in‑the‑loop frameworks and integration of ML with experimental data is a plus.

Required skills

• Python programming.
• Proficiency with PyTorch or TensorFlow.
• Machine learning techniques for sequence data.
• Understanding of transformer architectures.
• DNA sequence modelling and variant interpretation.