Lead Machine Learning Engineer📍 London, UK | HybridWe’re seeking a highly skilled Lead Machine Learning Engineer to help shape the future of personalised recommendation systems and deliver efficient, low-latency solutions at global scale. This role will work closely with data scientists, engineers, and product managers to design intelligent content recommendation mechanisms and drive the ongoing advancement of our Machine Learning Platform.What you’ll doModel Development: Design, train, and optimise machine learning models for personalisation, including recommendation engines, ranking algorithms, user segmentation, and content analysis.Data Pipeline Engineering: Build and maintain scalable data pipelines for feature engineering and model training across structured and unstructured datasets.Production Deployment: Deploy and monitor ML models in production, ensuring availability, performance, and ongoing relevance.Experimentation: Lead A/B testing and offline experiments to evaluate and improve model efficacy.Collaboration: Partner with cross-functional teams to align ML initiatives with business needs and user outcomes.Research & Innovation: Stay ahead of emerging trends in ML, deep learning, and generative AI, assessing opportunities for integration.What you’ll bringProven expertise across the full ML lifecycle, from development to deployment and monitoring.Advanced proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch).Experience with large-scale data processing and real-time streaming architectures.Strong understanding of recommendation system design and personalisation algorithms.Familiarity with generative AI in production use cases.Excellent communication and analytical problem-solving skills.Experience mentoring and guiding junior engineers.Why joinYou’ll be part of a global tech environment powering video streaming services used by millions worldwide. From personalised recommendations to next-generation ML platforms, this role offers the opportunity to make a direct impact at scale. Expect hybrid working, cutting-edge projects, and a collaborative culture that values innovation and continuous learning.