Senior Machine Learning Scientist

Job Title: Senior Machine Learning Scientist – BioAI 

Department: BioAI 

Location: London, UK 

Your impact begins here 

The ML Scientist will develop AI methods for biological discovery, focusing on cellular foundation models and complex biological data. You will research model architectures and training approaches, adapt models to priority discovery questions, and work with experimental teams to validate predictions. The role combines hands-on method development with delivering reproducible tools for scientists. You will guide junior colleagues and share research through presentations and publications. 

What you’ll bring 

• PhD in relevant degree domain including machine learning, computational biology, bioinformatics, computer science, or a related quantitative or life science discipline, with substantial AI/ML experience. 

• Demonstrated deep learning research for biological data, with depth in at least one relevant domain such as single-cell omics, biological sequences, or cellular imaging. 

• Strong Python and PyTorch skills, including model implementation, training, fine-tuning, debugging, and GPU inference. 

• Practical experience with representation or self-supervised learning, including pretraining, adapting, or systematically evaluating biological foundation models. 

• Experience building reproducible analysis or ML pipelines using version control, testing, documented environments, and sound data management. 

• Strong statistical and model-evaluation skills, including appropriate baselines, validation design, batch effects, confounding, and uncertainty assessment. 

• Ability to own research projects and collaborate with experimental and computational colleagues to translate biological questions into useful models. 

• Clear written and verbal communication of methods, limitations, and biological implications to audiences with different expertise. 

• Evidence of research or software contributions through publications, accessible code, or examples of delivered scientific tools. 

What you’ll do 

• Develop biological foundation models and predictive methods for target identification and prioritization, disease biology, and mechanism-of-action studies. 

• Partner with experimental scientists to define biological questions, incorporate biological knowledge into models, and design experiments that test model predictions. 

• Define project objectives, milestones, and success criteria that connect model performance to research decisions and measurable scientific impact. 

• Work with data and engineering teams to curate and harmonize biological datasets, addressing metadata quality, batch effects, and reproducible processing. 

• Develop reusable workflows for model training and inference, support adoption by research teams, and assess improvements in decision quality and research efficiency. 

• Research model architectures and self-supervised training methods for single-cell, omics, and imaging data, including multimodal approaches where scientifically justified. 

• Train, fine-tune, and debug models in PyTorch on GPU and high-performance computing infrastructure, balancing performance with computational cost. 

• Explore generative and agentic AI workflows that support biological hypothesis generation, data interpretation, and experimental planning. 

• Benchmark against strong baselines using independent datasets and biologically meaningful endpoints; assess data leakage, generalization, uncertainty, and interpretability. 

• Communicate findings, model limitations, and recommendations clearly to experimental scientists, technical colleagues, and leadership. 

• Mentor colleagues through scientific guidance and code review, and maintain clear documentation, reproducible experiments, and responsible data and model use. 

• Contribute to publications, relevant intellectual property, and internal knowledge sharing, and evaluate emerging methods for their value to BioAI priorities. 

Who you’ll work with 

The BioAI team is part of Novo Nordisk’s AI and Digital Innovation (ADI) area. We develop AI methods and biological foundation models to identify therapeutic targets, understand disease mechanisms, and predict cellular responses. We combine single-cell and multi-omics data with imaging and perturbation experiments to generate testable hypotheses. Working with experimental scientists, computational biologists, and engineers across Novo Nordisk and external partners, we translate these insights into better decisions and faster therapeutic discovery. 

We value a diverse and inclusive culture where colleagues collaborate, share ideas, and help each other grow. Our work starts and ends with the patient, with a focus on chronic diseases such as diabetes and obesity. We encourage ingenuity and innovation in an enjoyable workplace, supported by competitive compensation and a healthy work-life balance. 

What you can expect here 

This role is based in our London office, 3 days a week, with travel to Denmark about once or twice a year. 

0–10% overnight travel required. 

You will be trusted to own your work and supported to perform at your best. Leading some projects and contributing to others gives you room to grow your skills, take on greater responsibility and make your voice heard, in a team that shares what it learns. 

Start now 

Your impact begins today. If you are ready to turn scientific questions into AI that moves drug discovery and development forward, apply for the Machine Learning Scientist – BioAI role by 11 November 2026. 

Application Support 

We are an equal opportunities employer, and we commit to an inclusive recruitment process and equality of opportunity for all our job applicants.  If you're a person with a disability, if you're neurodivergent, and need any adjustments to be made during the application and selection process, please send an email to  ZQUK@novonordisk.com. Please include your name, the role you are interested in and the type of adjustment your need. 

You do not need to attach a cover letter, but please include a few sentences in your resume or CV about why you are applying. To keep the process efficient and fair, please do not add a photo to your CV.