Machine Learning Engineer (UK)

WHAT YOU WILL BE DOING

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

WE’D LOVE TO MEET SOMEONE WITH

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 responsible

AN ADDED BONUS IF YOU HAVE

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)