AI & ML Engineering Specialist

Novo Nordisk

AI & ML Engineering Specialist developing foundational ML models to enhance drug discovery at Novo Nordisk. Bridging technical and scientific elements to deliver robust, production-ready systems.

Posted 5/15/2026full-timeLondon • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

Act as a technical specialist in designing, developing, and operating end-to-end machine learning and AI systems
Work with researchers to train and optimise large scale ML models, pipelines, and agentic workflows
Ensure the full ML lifecycle, from exploratory prototyping and experimentation to production deployment, monitoring, and continuous optimisation is implemented
Design, build & optimise autonomous and agent based systems in a biological research context where appropriate
Work closely with stakeholders across biology, AI research, and adjacent areas, to clarify requirements and translate complex needs into scalable ML and agent enabled solutions
Ensure technical, regulatory, and ethical excellence in all ML and agentic systems, embedding data protection, model governance, and responsible AI principles by design

Requirements

What you’ll need

Bachelor’s or Master’s degree in Computer Science, Bioinformatics, Data Science, or a related field
Significant hands-on experience in AI or Machine Learning Engineering, delivering production-grade ML solutions in complex environments
Strong proficiency in software engineering with Python and experience with high-performance computing and/or cloud platforms (AWS and/or Azure)
Hands-on experience with modern ML frameworks, such as PyTorch, Hugging Face, and SciKit-Learn, for training, evaluating and deploying large scale models
Proven experience designing and operating end-to-end ML pipelines, including ingestion, training, evaluation, deployment, monitoring
Experience with ML workflow orchestration and productionisation, e.g. using Nextflow or comparable orchestration frameworks
Familiarity with foundation models and/or self-supervised learning in scientific or life science domains
Practical experience with MLOps and CI/CD practices, including model versioning, automated validation, and lifecycle management
Demonstrated ability to collaborate with cross-functional stakeholders, translating complex technical work into practical outcomes

Benefits

Comp & perks

Opportunities to learn and develop
Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development
Inclusive recruitment process

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningAI systemsML lifecyclePythonhigh-performance computingcloud platformsPyTorchHugging FaceSciKit-LearnMLOps

Soft Skills

collaborationstakeholder engagementrequirement clarificationtranslation of complex needscommunication

Certifications

Bachelor’s degreeMaster’s degree