Machine Learning Engineer (UK)

At Bumble, we're building a world where all relationships are healthy and equitable, and machine learning is central to making that real for millions of people every day. As part of our Machine Learning team in Recommendations, you'll help shape intelligent systems that power meaningful connections, safer interactions, and more personalised experiences across our platform. As a Machine Learning Engineer, you'll own ML problems end-to-end, from data exploration through to production. You'll bring curiosity to how we experiment, iterate, and improve, and you'll role-model our values of Curiosity and Excellence by continuously raising the bar in how we build and apply AI.

AI is deeply embedded in how we evolve at Bumble. In this role, you'll work with modern machine learning and emerging AI techniques, contributing to scalable systems and helping ensure AI is applied thoughtfully and responsibly in what we ship.

• Explore, develop, and deliver cutting-edge technology using the latest advances in deep learning and machine learning to personalize recommendations at Bumble.
• Own defined problems end-to-end, from data exploration and feature engineering through to model training, evaluation, and production deployment.
• Apply modern ML frameworks (e.g., PyTorch or TensorFlow) to design, train, and optimise models in production environments.
• Contribute to experimentation frameworks, including A/B testing and offline evaluation, to iterate on model performance with an agile mindset.
• Maintain and monitor production models, diagnose issues, and iterate to keep them reliable at scale.
• Take ownership of delivering high-quality solutions and see work through from insight to impact, balancing speed and rigor.
• Apply responsible AI practices, ensuring fairness, transparency, and safety are considered in model development and deployment.

• Around 3 years of hands-on experience building and shipping machine learning models in production.
• Strong programming skills in Python and solid proficiency with an ML framework such as PyTorch or TensorFlow.
• Industry experience in researching or applying machine learning, especially if in recommender systems, ranking or personalisation.
• Good understanding of MLOps and infrastructure concepts: CI/CD for ML, feature stores, model serving, observability, and versioning.
• Familiarity with containerisation and cloud-native environments (e.g. Docker, Kubernetes, GCP).
• Familiarity with experimentation methodologies such as A/B testing and model evaluation techniques.
• Demonstrates an agile mindset, adapting approaches based on data and evolving priorities while maintaining focus on outcomes.
• Growing AI fluency, with the ability to independently apply ML techniques and emerging tools (including LLMs) to solve problems responsibly.

• Practical experience with recommendation systems, ranking, search or personalisation.
• Expertise in modern machine learning architectures (e.g., transformers, graph neural networks, contrastive learning, and multi-modal embeddings).