Senior Machine Learning Scientist- Single Cell

Senior Machine Learning Scientist (Single Cell)

About the Company

We are a technology-driven biotech company developing new medicines for serious diseases. We combine single-cell multi-omics from patient tissue, functional assays, and machine learning to understand disease from cause to cure.

We are growing and building a team to push the boundaries of drug discovery. You will work in interdisciplinary teams where biology, computation, and engineering come together to solve hard, unsolved problems, supported by integrated wet and dry lab facilities.

We are an equal opportunities employer and do not discriminate on the basis of gender, sexual orientation, marital or civil partnership status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age.

The Opportunity

We are seeking a Machine Learning Scientist who combines strong ML fundamentals with a deep understanding of biological data to build generative and predictive models of cellular behaviour. Your work will be central to understanding and controlling cellular decision-making and enabling new therapeutic strategies.

You will have access to multiomic and interventional datasets, advanced computational infrastructure, and interdisciplinary expertise. We use modern ML tooling, including agentic workflows, to speed up research iteration. This role offers the chance to advance generative modelling in complex, high-dimensional, noisy real-world systems, and to see your work tested directly in experimental biology.

Responsibilities

• Design and implement generative modelling approaches that learn intervention effects from diverse biological data, including single-cell perturbation experiments.
• Develop models that go beyond correlation, focusing on generalisation, counterfactual prediction, and experimental design.
• Collaborate with experimental teams to design and validate computational hypotheses, using iterative strategies to identify the highest-signal next experiment.
• Evaluate models for causal coherence, mechanistic fidelity, and usefulness in guiding real-world interventions, not just for fit.
• Communicate findings clearly to colleagues and stakeholders across disciplines.

Required Qualifications

• PhD in machine learning, computer science, statistics, or a related quantitative field.
• Strong methodological foundations in modern ML, with depth in at least one area relevant to modelling structured, high-dimensional data.
• Excellent Python skills and familiarity with scalable ML tooling and high-performance computing.
• Strong engineering practice, including implementing models from scratch, distributed training, profiling, and performance optimisation.
• Demonstrable experience training models at scale and meaningfully scaling architectures or training schemes.
• A track record of taking ideas to working, scaled implementations by adapting or designing models suited to the data rather than applying off-the-shelf methods.
• Comfort working with messy, noisy, real-world scientific data.

Personal Attributes

• Comfortable in a matrixed environment, balancing multiple stakeholders and contributing across teams.
• Takes ownership, proactively seeks ways to contribute, and helps others do their best work.
• Communicates openly and directly, gives and receives feedback constructively, and handles difficult conversations respectfully.
• Seeks out diverse perspectives, builds strong working relationships, and works toward shared goals.
• Approaches challenges with openness and resilience, and holds high standards.

Working Style

We work in a collaborative, interdisciplinary environment where impact comes from partnership across scientific, technical, and operational teams. You will contribute your expertise across multiple projects while aligning with shared priorities.