Bumble Inc.
Machine Learning Engineer (UK)
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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.
What you'll be doing
- 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.
We'd love to meet someone with
- 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.
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An added bonus if you have
- 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.
About Us
Bumble Inc. is the parent company of Bumble Date, BFF, and Badoo. The Bumble platform enables people to build healthy and equitable relationships, through Kind Connections. Founded by Whitney Wolfe Herd in 2014, Bumble was one of the first dating apps built with women at the center and connects people across dating (Bumble Date) and friendship (BFF). BFF is a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections and community based on shared interests. Badoo, which was founded in 2006, is one of the pioneers of web and mobile dating products.
AI Fluency
AI is important to us. We’re excited by people who are curious and experimental, and who think thoughtfully about how AI can amplify their impact and outcomes.
We encourage you to use AI responsibly as you prepare your application. Please don’t use it to fabricate experiences or answer questions live in interviews. We care deeply about authenticity and want to understand your real skills, judgment and voice, because building a meaningful, genuine connection with you matters to us.
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Final Compensation
Will be determined based on factors such as the selected candidate’s qualifications, relevant experience, skill set, and other job-related considerations.
Inclusion at Bumble Inc.
Bumble Inc. is an equal opportunity employer and we strongly encourage people of all ages, color, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply. We're happy to make any reasonable adjustments that will help you feel more confident throughout the process, please don't hesitate to let us know how we can help.
In your application, please feel free to note which pronouns you use (For example: she/her, he/him, they/them, etc).
AI in Bumble Inc. Hiring
At Bumble, we may use AI tools to support parts of our recruitment process — such as helping us record, transcribe, and summarize conversations, and supporting job alignment by comparing resumes and job descriptions to highlight skills and potential roles that may be a good match. These tools help us work more efficiently and stay focused on you during our conversations. Importantly, all hiring decisions are made by people. AI is used only to support our team’s efficiency and improve the candidate experience — not to evaluate or decide on your candidacy. Participation in AI-supported interviews and conversations is completely voluntary and will not impact your candidacy. If you’d prefer to opt out, simply let your recruiter or interviewer know at the start of a call, or anytime during the interview or conversation. Summaries and related data are retained only as long as needed in line with our internal data retention policies. If at any point you’d like a transcription or summary deleted, please contact your recruiter directly.
Fraudulent Candidate Detection
Our applicant tracking system analyzes signals relating to device, IP, email, and phone data associated with each application to protect applicants and our hiring process from fraudulent applications. These signals provide indicators for internal review only and do not constitute a definitive determination of identity or intent. No applicant is rejected, advanced, or otherwise affected based solely on this analysis without human review.
For further information on how we hold and manage your data, please refer to our Privacy Policy.
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