At a Glance
- Tasks: Design and apply advanced machine learning techniques to DNA and genetic data.
- Company: Join Relation, a pioneering TechBio company transforming medicine through innovative technology.
- Benefits: Competitive salary, inclusive culture, and opportunities for impactful work in drug discovery.
- Other info: Collaborative environment with exceptional career growth and a commitment to diversity.
- Why this job: Make a real difference in patients' lives by advancing therapeutic discovery with cutting-edge ML.
- Qualifications: PhD in machine learning or related field, experience with biological sequences, and Python proficiency.
The predicted salary is between 60000 - 80000 £ per year.
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 program. 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 partnership 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 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.
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.
Your Responsibilities
- 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.
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 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.
Desirable Knowledge or Experience
- 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
Join us in this exciting role where your contributions will have a direct impact on advancing our understanding of genetics and disease risk, supporting our mission to bring transformative medicines to patients. Together, we’re not just doing research; we’re setting new standards in the field 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 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.
Machine Learning Scientist – Sequence Modelling in London employer: Relationrx
Relation is an exceptional employer, offering a dynamic and inclusive work culture that fosters collaboration across interdisciplinary teams in the heart of London. With state-of-the-art facilities and a commitment to employee growth, you will have the opportunity to drive innovative research in a rapidly scaling TechBio company, making a meaningful impact on drug discovery and patient outcomes. Join us to be part of a mission that not only values your expertise but also encourages you to thrive in a supportive environment where diverse perspectives are celebrated.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Scientist – Sequence Modelling in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at Relation or similar companies. Attend meetups, webinars, or conferences related to machine learning and biotech. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to genetics or biological data. This will give you an edge and demonstrate your hands-on experience when you chat with potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both scientists and techies. Mock interviews can be super helpful!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining our team at Relation. Let’s make a difference together!
We think you need these skills to ace Machine Learning Scientist – Sequence Modelling in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Scientist role. Highlight your experience with machine learning techniques, especially in relation to DNA and genetic data. We want to see how your skills align with our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about genetics and machine learning. Share specific examples of your work that demonstrate your fit for the role and how you can contribute to our team.
Showcase Your Projects:If you've worked on relevant projects, make sure to include them! Whether it's research papers, personal projects, or contributions to open-source, we love seeing practical applications of your skills. It helps us understand your hands-on experience.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re genuinely interested in joining our innovative team at Relation!
How to prepare for a job interview at Relationrx
✨Know Your Machine Learning Stuff
Make sure you brush up on the latest machine learning techniques, especially those related to sequence modelling and genetics. Be ready to discuss your experience with Python and ML platforms like PyTorch or TensorFlow, as well as any projects you've worked on that relate to DNA sequences.
✨Show Your Collaborative Spirit
Since this role involves working closely with both computational and experimental scientists, be prepared to share examples of how you've successfully collaborated in the past. Highlight your ability to communicate complex ideas clearly and how you’ve contributed to team success.
✨Prepare for Technical Questions
Expect some technical questions that test your understanding of transformers and their applications in biomedical research. Brush up on lab-in-the-loop frameworks and be ready to discuss how you would integrate ML techniques with experimental data.
✨Demonstrate Your Passion for Impact
Relation is all about making a difference in patients' lives, so convey your passion for using machine learning to advance therapeutic discovery. Share your motivations and how you see your work contributing to meaningful outcomes in healthcare.