Bumble Inc.

Senior Machine Learning Engineer, Recommendations

13 November 2024
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Deadline date:
£149000 / year

Job Description

Bumble Inc. is the parent company of Bumble, Badoo, Fruitz and Official. The Bumble platform enables people to build healthy and equitable relationships, through kind connections. Founded by CEO 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), friendship (Bumble BFF) and professional networking (Bumble Bizz). Badoo, which was founded in 2006, is one of the pioneers of web and mobile dating products. Fruitz, founded in 2017, encourages open and honest communication of dating intentions through playful fruit metaphors. Official is an app for couples that promotes open and honest communication between partners and was founded in 2020.
Are you ready to revolutionise the way people find meaningful connections? Bumble is looking for a Machine Learning Engineer to join our People Recommendations team and play a key role in building the next generation recommendation platform, empowering millions of users across Bumble Inc.’s apps to discover love and friendship through innovative, cutting-edge solutions.We are looking for talents with a broad range of ML algorithms and rich hands-on experience in creating varied ML systems. You will have the opportunity to explore, develop and deploy state of the art ML solutions that will redefine how people connect and form relationships online. The ideal candidate combines strong product sense, extensive experience in a variety of machine learning applications, and a passion for creating impactful technology. If you’re passionate about leveraging AI to shape the future of online connections, we want to hear from you!

THE RECOMMENDATIONS TEAMWe are part of the cross-functional Recommendations group at Bumble Inc., a team of passionate machine learning professionals, software engineers and data scientists who focus on designing and building products that power our mission of “creating a world where all relationships are healthy and equitable, through Kind Connections.” We partner with wider business stakeholders, Product, and other Engineering teams to build state-of-the-art recommendation systems for our portfolio of apps, including Bumble, Badoo, BFF, and Fruitz. We are passionate about improving the experience of our members through leveraging AI and Machine Learning in our products.

What you will be doing

  • Explore, develop and deliver new cutting-edge solutions for ML recommendations systems.
  • Leverage technology like GNNs, Deep Neural Networks, etc. to create bespoke solutions for complex problems.
  • Set up and conduct large-scale experiments to test hypotheses and drive product development.
  • Working with our MLOps platform directly to efficiently serve models at a global scale.
  • Deploy models, and lead their continuous monitoring & improvement.
  • Keep up with state-of-the-art research, with the opportunity to create prototypes for the business.
  • Work in a cross-functional team alongside data scientists, machine learning engineers, and both technical and non-technical stakeholders.

About You

  • An advanced degree in Computer Science, Mathematics or a similar quantitative discipline
  • Hands-on experience in delivering machine learning models to production at scale
  • Demonstrated ability to develop innovative technical solutions to complex problems 
  • Experience in writing production-quality Python code
  • Comfortable working with classic ML frameworks, such as Pytorch or TensorFlow
  • Strong understanding of machine learning applications development life cycle processes and tools: CI/CD, version control (git), testing frameworks, MLOps, agile methodologies, monitoring and alerting
  • Comfortable working with Docker and containerised applications
  • Strong communication skills, and the ability to work collaboratively and proactively in a fast-paced environment alongside technical and non-technical stakeholders
  • A genuine passion for Machine Learning, and a thoughtful approach to AI fairness, accountability, and transparency.