Senior ML Scientist – Single-Cell Genomics & Generative AI

Hlx Life Sciences; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Hlx Life Sciences in London is seeking a Senior Machine Learning Scientist to advance generative and predictive models for single-cell biology. You will work across interdisciplinary teams blending biology, computation, and engineering to push drug discovery forward.

The role emphasizes models that generalise beyond correlations, with a focus on causal insights, experimental design, and scalable training on large real-world datasets. You will communicate findings across disciplines.

Qualifications

• PhD in machine learning, computer science, statistics, or a related quantitative field.
• Strong ML foundations with depth in at least one area for high-dimensional data modelling.
• Excellent Python skills and familiarity with scalable ML tooling and HPC.
• Strong engineering practice: implement models from scratch, distributed training, profiling, and optimization.
• Experience training models at scale and scaling architectures or training schemes.
• Track record of taking ideas to working implementations tailored to data.
• Comfort working with messy, noisy real-world scientific data.

Responsibilities

• Design and implement generative modelling approaches learning intervention effects from diverse biological data.
• Develop models that generalise, with counterfactual prediction and experimental design.
• Collaborate with experimental teams to design and validate computational hypotheses.
• Evaluate models for causal coherence, mechanistic fidelity, and real-world usefulness.
• Communicate findings clearly to colleagues across disciplines.

Skills

Python

Distributed training

High-Dimensional data

Model scaling

Experiment design

Biological data

Education

PhD in machine learning, computer science, statistics, or related quantitative field

Job description

Hlx Life Sciences in London is seeking a Senior Machine Learning Scientist to advance generative and predictive models for single-cell biology. You will work across interdisciplinary teams blending biology, computation, and engineering to push drug discovery forward.

The role emphasizes models that generalise beyond correlations, with a focus on causal insights, experimental design, and scalable training on large real-world datasets. You will communicate findings across disciplines.