Machine Learning Engineer (UK)

Bumble is looking for a Machine Learning Engineer (Computer Vision) to join our Trust & Safety team and play a key role fulfilling our mission to create a world where all relationships are healthy and equitable. Concretely, this means implementing, deploying and maintaining state of the art machine learning models, particularly in computer vision, that help Bumble provide a safe and engaging experience for our users and improve the way Bumble operates.

With millions of images and messages exchanged on our platform every day, there is a wealth of opportunity to make a real difference in this role and help people to find love all over the world! The ideal candidate combines strong business acumen, extensive experience in machine learning applications (specifically in Computer Vision) along with a passion for tech.

WHAT YOU WILL BE DOING

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