Machine Learning Jobs

• London
• On-site
• Senior
• Permanent

Develop and benchmark machine learning models for DNA sequence, regulatory elements and genetic variation, including variant effect prediction and representation learning. Integrate sequence-derived representations with transcriptomic, epigenomic and perturbational data to uncover disease mechanisms and support target prioritisation, working within the interdisciplinary Rosalind team.

DNA Language Models · Genomic Foundation Models · Transformer +2

• Oxford
• On-site
• £39,424 – £47,779 pa
• Entry level
• Contract

Develops novel statistical and machine learning methods for large-scale genomic, phenotypic and health data, covering areas such as genomic language models, multimodal and federated learning, and multi-ancestry methods. The role involves building scalable, reproducible research software, applying it to biobank-scale datasets, publishing findings and guiding junior researchers within the Palamara Group.

• London
• On-site
• Entry level
• Permanent

Contributes to the design and execution of cellular and biochemical assays, including arrayed CRISPR perturbation screens, cell culture of primary and immortalised lines, and phenotypic readouts such as flow cytometry, RT-qPCR and ELISA. Also handles data analysis, sample tracking, QC, reagent stocks and lab upkeep, working closely with Discovery and computational colleagues in a matrixed TechBio setting.

CRISPR · Flow Cytometry · RT-qPCR +3

• Exeter
• £21,805 pa
• Entry level
• Contract

Builds next-generation machine learning models to predict genetic interactions from large-scale perturb-seq data, evaluating graph neural networks and autoencoders for genome-wide scalability and cross-cancer generalisation. Works in a lab-in-the-loop cycle with AstraZeneca and Cancer Research Horizons FGC, who generate pancreatic cancer perturb-seq data to fine-tune the computational models. Purely computational, with high performance computing and industry-aligned experience.

Perturb-seq · DepMap · Project Score

• Oxford
• On-site
• £40,178 – £48,700 pa
• Entry level
• Contract

Develops and validates machine-learning models on large-scale multimodal NHS clinical data, including electronic health records, genomics, imaging and digital pathology. Builds reproducible pipelines for heterogeneous longitudinal data within secure environments, working with clinicians, pathologists and engineers on disease progression and treatment response prediction.

NHS Data · Electronic Health Records · Secure Data Environment +2

• London
• Director · manages people
• Permanent

Lead the platform engineering team owning hybrid on-prem and cloud compute, Kubernetes clusters, and MLOps infrastructure such as Kubeflow notebooks, GPU training and model serving. Set architectural direction, mature CI/CD, observability and security controls, and ensure scientific compute meets the needs of ML and data science teams. Reports to the VP of Engineering in a matrixed TechBio environment.

Kubernetes · Kubeflow · ISO 27001 +2