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Machine Learning Scientist - Rosalind

Relation
London
2 weeks ago
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Join to apply for theMachine Learning Scientist - Rosalindrole atRelation

About Relation

Relation is an end-to-end biotech company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics directly from patient tissue, functional assays, and machine learning to drive disease understanding—from cause to cure.

Machine Learning Scientist – Rosalind

About Relation

Relation is an end-to-end biotech company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics directly from patient tissue, functional assays, and machine learning to drive disease understanding—from cause to cure.

This year, we embarked on an exciting dual collaboration with GSK to tackle fibrosis and osteoarthritis, while also advancing our own internal osteoporosis programme. By combining our cutting-edge ML capabilities with GSK’s deep expertise in drug discovery, this partnership underscores our commitment to pioneering science and delivering impactful therapies to patients.

We are rapidly scaling our technology and discovery teams, offering a unique opportunity to join one of the most innovative TechBio companies. Be part of our dynamic, interdisciplinary teams, collaborating closely to redefine the boundaries of possibility in drug discovery. Our state-of-the-art wet and dry laboratories, located in the heart of London, provide an exceptional environment to foster interdisciplinarity and turn groundbreaking ideas into impactful therapies for patients.

We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. We cultivate innovation through collaboration, empowering every team member to do their best work and reach their highest potential.

By joining Relation, you will become part of an exceptionally talented team with extraordinary leverage to advance the field of drug discovery. Your work will shape our culture, strategic direction, and, most importantly, impact patients’ lives.

The Opportunity

As a Machine Learning Scientist within the Rosalind team, you will design and apply advanced machine learning techniques to DNA and genetic data. This role is ideal for someone with a strong machine learning background and an interest in genetics. Your work will directly contribute to uncovering non-trivial associations between genetic variants and diseases, ultimately advancing therapeutic discovery.

The Team You Will Join

The Rosalind team sits at the cutting edge of using DNA models to identify genes involved in diseases that have not been previously recognised. Think of the team as the starting point of a discovery funnel: the Rosalind team’s work feeds this funnel by using DNA models to uncover genes involved in diseases. The day-to-day focus is on identifying non-trivial associations between genetic variants and diseases, providing foundational insights for further therapeutic development.

It’s an exciting opportunity to contribute to groundbreaking research and help build the best possible models for advancing disease understanding.

Your Responsibilities

  • Develop and apply machine learning techniques to analyse genetic data and DNA models.
  • Collaborate with experimental teams and computational biologists to design, test, and validate ML-driven hypotheses.
  • Implement transformers and other ML techniques to address challenges in biological sequences, such as DNA modelling.
  • Contribute to building foundational models with external data and fine-tune them using disease-specific internal data.
  • Stay updated with the latest developments in machine learning and genomics to innovate and optimise model development.

Professionally, You Have

  • A PhD in machine learning, computational biology, or a related field, or equivalent industrial experience.
  • Demonstrated experience applying machine learning techniques to DNA or genetic data. Alternatively, expertise in using transformers for biological data, such as DNA, protein, or evolutionary-scale modelling.
  • 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.

Desirable Knowledge Or Experiences

  • Experience with 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

  • Inclusive leader and team player.
  • Clear communicator.
  • Driven by impact.
  • Humble and hungry to learn.
  • Motivated and curious.
  • Passionate about making a difference in patients’ lives.

Join us in this exciting role, where your contributions will directly impact advancing our understanding of genetics and disease risk, supporting our mission to deliver transformative medicines to patients. Together, we’re not just conducting research—we’re setting new standards in the fields of machine learning and genetics. The patient is waiting!

Relation is a committed equal opportunities employer.

RECRUITMENT AGENCIES: Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.

Seniority level

  • Seniority levelEntry level

Employment type

  • Employment typeFull-time

Job function

  • Job functionOther

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