As a Senior Machine Learning Engineer, you will take ownership of designing, building, and deploying scalable ML solutions that process complex physiological and biological data. Working across the full model lifecycle, you will collaborate closely with cross-functional teams to turn research and data into practical, production-ready applications. You will be part of a technically strong team where your work directly contributes to advancing data-driven health and performance solutions. This is an ideal opportunity for someone looking to move beyond pure development into impactful ML engineering with tangible real-world outcomes. Key Responsibilities: Design, build, and deploy machine learning models for physiological and performance-related predictions Develop and maintain end-to-end ML pipelines, including data ingestion, feature engineering, model training, and deployment Work with large-scale physiological and genetic datasets to extract actionable insights Collaborate with cross-functional teams to translate domain expertise into scalable ML solutions Optimise model performance, scalability, and reliability in production environments Implement model monitoring, evaluation, and retraining processes Ensure strong data quality, governance, and reproducibility standards Job Experience and Skills Required: Education: Bachelors degree in Computer Science, Engineering, Data Science, or a related field (Postgraduate qualification advantageous) Experience: 5 years experience in Machine Learning Engineering, Applied AI, or Data Science Proven experience building and deploying production-grade machine learning models Experience working with large datasets and data pipelines Exposure to health, physiological, or biological data environments is advantageous Skills: Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn) Solid understanding of machine learning concepts and model evaluation techniques Experience with data processing tools (Pandas, NumPy, Spark) Knowledge of MLOps practices (CI/CD, model versioning, monitoring) Experience with cloud environments (AWS, Azure, or GCP) Strong grounding in statistics and analytical thinking Apply now! For more exciting IT vacancies, please visit: