About the Role
As a Machine Learning Scientist in the Rosalind team, you will develop and apply cutting-edge ML techniques to genetic data, contributing directly to the discovery of non-obvious links between genetic variants and diseases to advance new therapies.
Responsibilities
- Design and implement machine learning methods for modeling DNA sequences
- Train, fine-tune, and assess models for interpreting genetic variants, identifying genes, and modeling regulatory elements
- Work alongside computational and experimental researchers to test hypotheses generated by machine learning
- Utilize large internal and external datasets to adapt models for disease-specific applications
- Create rigorous evaluation frameworks to assess model performance, biological significance, and real-world applicability
- Advance scientific progress by integrating recent developments in machine learning and genomics
Requirements
- PhD in machine learning, computational biology, or a related discipline, or equivalent experience in industry
- Proven track record applying machine learning to biological sequences or analogous data such as text
- Strong programming skills in Python and experience with at least one ML framework such as PyTorch or TensorFlow
- Adaptability and problem-solving ability when working at the interface of biology and machine learning
Nice to Have
- Experience using machine learning on biological sequences like DNA or proteins
- Deep knowledge of transformer architectures and their use in biomedical contexts
- Familiarity with lab-in-the-loop systems and integrating machine learning with experimental data workflows
Work Arrangement
On-site — London
Team
The Rosalind team specializes in extracting biological insights from DNA representations, focusing on variants, genes, and regulatory regions. It operates at the intersection of genomics and machine learning, building models that detect meaningful biological signals and feed into target discovery pipelines. The team has published at top ML conferences, including a Best Paper award at NeurIPS AI4D3 for PatchDNA and recent work at ICLR, demonstrating leadership in DNA representation learning. This is a unique chance to shape cutting-edge research and develop state-of-the-art models for understanding disease biology.
Join us
Take on a role where your work directly advances genetic and disease understanding, supporting the mission to deliver transformative medicines. This is more than research—it’s about setting new benchmarks in machine learning and genomics. The patient is waiting.
Other
- The company is an equal opportunities employer.
- Recruitment agencies should not submit unsolicited CVs. Do not send CVs to job email addresses or employees. No fees will be paid for unsolicited submissions.
