Welcome to Next Phase Recruitment! Please see below our current jobs that match your search criteria. For a broader job search please visit the home page or call us on 01403 216216 to discuss career options in other areas of Life Science and Technology.

Welcome to Next Phase Recruitment! Please use the above link to see our current jobs that match your search criteria. For a broader job search please visit the home page or call us on 01403 216216 to discuss career options in other areas of Life Science and Technology.

​Senior / Principal Machine Learning Engineer

Senior and Principal Machine Learning Engineers play a pivotal role in developing and deploying advanced algorithms that support innovation in biotechnology, pharmaceuticals, diagnostics, and digital health. They apply artificial intelligence (AI) and data science methods to extract meaningful insights from complex biological and clinical data.

Their work accelerates drug discovery, optimizes clinical trials, and enhances personalized medicine solutions.

Typical responsibilities include:

  • Designing and training machine learning models – Engineers build models for predictive analytics, image recognition (e.g., pathology slides), sequence analysis (e.g., genomics), or molecular property prediction using techniques such as deep learning, ensemble models, or NLP.

  • Handling large-scale datasets – This includes genomic data, high-throughput screening outputs, patient health records, and real-time sensor data. Engineers are responsible for data preprocessing, normalization, and augmentation.

  • Developing production-grade ML pipelines – They create scalable and reproducible workflows using tools like TensorFlow, PyTorch, Scikit-learn, XGBoost, and cloud-native ML Ops stacks (e.g., SageMaker, MLflow).

  • Collaborating across cross-functional teams – ML Engineers partner with data scientists, bioinformaticians, software developers, and domain scientists to ensure models are scientifically valid, clinically useful, and production-ready.

  • Interpreting and validating models – Especially in regulated life science settings, model transparency and explainability (e.g., SHAP, LIME) are crucial for adoption and compliance.

Areas of specialization may include:

  • Drug discovery and design – Predicting molecular activity, ADME-Tox properties, or drug-target interactions.

  • Clinical outcome modeling – Building models for disease progression, biomarker identification, or patient stratification.

  • Image-based diagnostics – Using deep learning on histology, radiology, or microscopy images.

  • Omics integration – Combining multi-omics data (genomics, transcriptomics, proteomics) to derive insights.

Senior and Principal Engineers are expected to lead technical strategy, mentor junior team members, and often serve as key contributors to publications, patents, or regulatory submissions.

They typically bring expertise in Python, R, and cloud infrastructure (e.g., AWS, GCP), along with strong statistical foundations and familiarity with biomedical data privacy standards such as HIPAA or GDPR.