Machine Learning Engineer, Recommendations

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. With millions of images and connections formed on our platform every day, there is a wealth of opportunity to make a difference all over the world!

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 TEAM

We 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.

We'd love to meet someone with:

• 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.

Bonus points for:

• Experience working with recommendation systems a strong plus
• Experience building and deploying computer vision pipelines using common libraries, frameworks, and deep learning algorithms (CNNs, representation learning, etc.).
• Experience working with modern LLM deployment frameworks and libraries, such as HuggingFace TGI, VLLM, TensorRT-LLM, or similar
• Understanding of the concepts of GPU-powered workloads, NVIDIA drivers, container runtimes
• Experienced at deploying highload ML applications on container orchestrators (bare-metal k8s, GKE, EKS)
• Publications in top Machine Learning conferences
• Knowledge of statistics, data visualisation, and A/B testing.

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