relationrx - Machine Learning Scientist – Sequence Modelling
Requirements
• A PhD in machine learning, computational biology, or a related field, or equivalent industrial experience • Demonstrated experience applying machine learning techniques to biological sequences or text • Proficiency in Python and at least one ML platform (e.g. PyTorch, TensorFlow) • Flexibility and the ability to tackle new challenges at the intersection of biology and machine learning. • Experience applying machine learning to biological sequences, including DNA or proteins • Strong understanding of transformers and their applications in biomedical research • Knowledge of lab-in-the-loop frameworks and integration of ML techniques with experimental data. • PERSONALLY, YOU ARE • An inclusive leader and team player • A clear communicator • Driven by impact • Humble and eager to learn • Motivated and curious • Passionate about making a difference in patients’ lives • Relation is a committed equal opportunities employer. • RECRUITMENT AGENCIES • Please note that Relation does not accept unsolicited CVs from agencies. CVs should not be forwarded to our job aliases or employees. Relation Therapeutics will not be liable for any fees associated with unsolicited CVs.
Responsibilities
• The Rosalind team aims to extract useful insights through representations of DNA, whether related to variants, genes or the regulatory mechanisms in between. Sitting at the forefront of ML for genomics, the team develops models that help uncover meaningful biological signals from DNA and turn them into foundations for our target discovery pipelines. • The team also has a strong track record of publishing at major ML venues, including winning a Best Paper award for PatchDNA at the NeurIPS AI4D3 workshop and publishing recently in the main conference track at ICLR: https://iclr.cc/virtual/2026/poster/10011056. • It’s an exciting opportunity to contribute to cutting-edge research, advance representation learning for DNA, and help build state-of-the-art models for understanding biology and disease. • Develop and apply sequence modelling machine learning techniques to DNA sequences • Train, fine-tune and evaluate DNA sequence models for tasks including 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 and adapt models for disease-focused applications • Design robust evaluations to measure model quality, biological relevance and translational value • Contribute to scientific innovation by applying the latest advances in machine learning and genomics.
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