Job Overview

logoAre 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
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 systemsLeverage technology like GNNs, Deep Neural Networks, etc. to create bespoke solutions for complex problemsSet up
and conduct large-scale experiments to test hypotheses and drive product developmentWorking with our MLOps platform directly to efficiently serve models at a global scaleDeploy models, and lead their continuous monitoring
& improvementKeep up with state-of-the-art research, with the opportunity to create prototypes for the businessWork 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 disciplineHands-on experience in delivering machine learning models to
production at scaleDemonstrated ability to develop innovative technical solutions to complex problems Experience in writing production-quality Python codeComfortable working with classic ML frameworks, such as Pytorch or
TensorFlowStrong understanding of machine learning applications development life cycle processes and tools: CI/CD, version control (git), testing frameworks, MLOps, agile methodologies, monitoring and alertingComfortable
working with Docker and containerised applicationsStrong communication skills, and the ability to work collaboratively and proactively in a fast-paced environment alongside technical and non-technical stakeholdersA 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 plusExperience 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 similarUnderstanding of the concepts of GPU-powered workloads, NVIDIA drivers, container runtimesExperienced at deploying highload ML applications on container orchestrators (bare-metal k8s, GKE,
EKS)Publications in top Machine Learning conferencesKnowledge of statistics, data visualisation, and A/B testing

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