Machine Learning Engineer

On behalf of a global pharmaceutical organisation, I am seeking a Senior Machine Learning Engineer to help scale and operationalise AI/ML innovation. You will work at the interface of cutting-edge data science and robust engineering, partnering closely with AI/ML scientists to transition exploratory research into production-ready, repeatable ML solutions.

This is an amazing opportunity to immerse yourself in a vibrant tech ecosystem while contributing to the transformation of AI/ML in the pharmaceutical industry.

Role Responsibilities:

• Partner directly with AI/ML scientists to optimise models and deploy solutions into production, acting as an internal consultant from prototype to platform.
• Translate exploratory work into robust ML pipelines, creating blueprints and best practices for scalable, repeatable machine learning.
• Explore, analyse, and visualise data to understand distributions and identify issues that may impact real-world model performance.
• Ensure data quality and model reliability through validation strategies, cleaning pipelines, and systematic testing.
• Build and improve training pipelines and reusable ML components, addressing errors and technical debt.
• Collaborate with ML Infrastructure engineers to co-develop ML platforms, strengthen MLOps capabilities, and upskill teams across the organisation.

• You are a technically strong, collaborative engineer with experience working alongside data scientists and life-science researchers.
• PhD or Master's degree with relevant experience, or a Bachelor's degree with strong, hands-on expertise in ML engineering.
• Experience working in a healthcare or life-science environment would be advantageous, but not essential.
• Advanced Python skills and hands-on experience with data analytics and deep learning tools such as scikit-learn, Pandas, PyTorch, Jupyter, and ML pipelines.
• Practical experience with modern data and ML tooling, including Databricks, Ray, vector databases, Kubernetes, and workflow orchestrators such as Apache Airflow, Dagster, or Astronomer.
• Experience with GPU computing, on-premise and/or in the cloud, and building end-to-end scalable ML infrastructure.
• Strong knowledge of AWS and/or Azure, containerisation, Kubernetes, automation/DevOps, and the full ML lifecycle.
• Practical expertise in data wrangling and integration of large, heterogeneous datasets relevant to drug discovery.
• Hands-on experience with large language models, including fine-tuning, DPO, training, hosting, RAG pipelines, vector databases, and multi-agent systems.
• A proven track record of building, training, and deploying production-grade ML models in industry and/or academic research.