Machine Learning Engineer (UK) at Bumble Inc. in UK London

Job description

• Application
• Machine Learning Engineer — Matching & Recommendations
• At Bumble, we’re building the Love Company — a place where healthy, equitable relationships and friendships can start and grow. Machine Learning plays an important role in that mission, powering the matching, recommendation and personalisation experiences that help our members discover meaningful connections.
• As a Machine Learning Engineer, Matching & Recommendations, you’ll work hands-on to develop and improve the ML systems behind these experiences. You’ll own well-defined ML problems end-to-end — from exploring data and developing models through experimentation, deployment and monitoring — while learning from experienced engineers and contributing to the evolution of Bumble’s recommendation platform. This is an opportunity to apply modern ML techniques to real-world problems at scale and see the direct impact of your work on our members.

Responsibilities

• Explore, develop and deliver modern Machine Learning solutions that improve recommendations, matching and personalisation across Bumble.
• Own defined ML problems end-to-end, from data exploration and feature engineering through model training, evaluation, production deployment and iteration.
• Use modern ML frameworks such as PyTorch or TensorFlow to design, train and optimise models for production environments.
• Contribute to experimentation, including A/B testing and offline evaluation, using results to continuously improve model and product performance.
• Build, maintain and monitor production models, diagnosing issues and improving reliability and performance at scale.
• Write high-quality, maintainable code and contribute to code reviews, technical discussions and engineering best practices.
• Take ownership of delivering high-quality solutions from insight through to measurable impact, balancing speed, quality and technical rigour.
• Apply responsible AI practices, considering fairness, transparency, privacy and member safety throughout model development and deployment.
• Collaborate with Machine Learning, Engineering, Product and Data partners to translate member and product problems into practical ML solutions.

Requirements

• Around 3+ years of hands-on experience building and shipping Machine Learning models in production, although we welcome candidates with alternative backgrounds who demonstrate equivalent skills and experience.
• Strong programming skills in Python and proficiency with an ML framework such as PyTorch or TensorFlow.
• Industry experience researching or applying Machine Learning, ideally in areas such as recommendation systems, ranking, retrieval, search or personalisation.
• Good understanding of the ML development lifecycle, including data and feature development, training, evaluation, deployment, monitoring and iteration.
• Understanding of MLOps and infrastructure concepts such as CI/CD for ML, feature stores, model serving, observability and versioning.
• Familiarity with containerisation and cloud-native environments such as Docker, Kubernetes and GCP, or comparable technologies.
• Familiarity with experimentation methodologies, including A/B testing, offline evaluation and model performance metrics.
• Strong problem-solving skills and the ability to independently navigate defined but technically ambiguous problems.
• An agile mindset, adapting your approach based on data, experimentation and evolving priorities whilst maintaining focus on outcomes.
• Growing AI fluency, with the ability to independently apply ML techniques and emerging technologies, including LLMs where appropriate, responsibly and effectively.
• Practical experience building recommendation systems, ranking, retrieval, search or personalisation systems in production.
• Experience with modern recommendation approaches such as embeddings, two-tower models, learning-to-rank or representation learning.
• Exposure to modern ML architectures and techniques such as transformers, graph neural networks, contrastive learning or multimodal embeddings.
• Experience working with real-time or low-latency ML inference systems at scale.

Skills

Required

• Python
• PyTorch
• TensorFlow
• MLOps
• CI/CD for ML
• Feature stores
• Model serving
• Observability
• Versioning
• Docker
• Kubernetes
• GCP

Preferred

• Recommendation systems
• Ranking
• Retrieval
• Search
• Personalisation
• Embeddings
• Two-tower models
• Learning-to-rank

Benefits

• Will be determined based on factors such as the selected candidate’s qualifications, relevant experience, skill set, and other job-related considerations.