machine learning engineer

Bumble Inc. operates Bumble Date, BFF, and Badoo, platforms that help people build healthy and equitable dating, friendship, and community relationships through digital connections.

• Explore, develop, and deliver deep learning and machine learning technology for personalized recommendations at Bumble;
• Own defined problems end-to-end, from data exploration and feature engineering through model training, evaluation, and production deployment;
• Design, train, and optimize production models using modern ML frameworks such as PyTorch or TensorFlow;
• Contribute to experimentation frameworks, including A/B testing and offline evaluation, to improve model performance;
• Maintain and monitor production models, diagnose issues, and iterate to keep them reliable at scale;
• Take ownership of high-quality solutions from insight to impact while balancing speed and rigor;
• Apply responsible AI practices, considering fairness, transparency, and safety in model development and deployment.

• Around 3 years of hands-on experience building and shipping machine learning models in production;
• Strong programming skills in Python;
• Solid proficiency with an ML framework such as PyTorch or TensorFlow;
• Industry experience researching or applying machine learning;
• Good understanding of MLOps and infrastructure concepts, including CI/CD for ML, feature stores, model serving, observability, and versioning;
• Familiarity with containerization and cloud-native environments such as Docker, Kubernetes, and GCP;
• Familiarity with experimentation methodologies such as A/B testing and model evaluation techniques;
• Agile mindset with the ability to adapt approaches based on data and evolving priorities while focusing on outcomes;
• Ability to independently apply ML techniques and emerging tools, including LLMs, responsibly;
• Nice to have: practical experience with recommendation systems, ranking, search, or personalization; expertise in modern machine learning architectures such as transformers, graph neural networks, contrastive learning, and multimodal embeddings.

• Final compensation will be determined based on the selected candidate’s qualifications, relevant experience, skill set, and other job-related considerations;
• Equal opportunity employer with reasonable adjustments available throughout the process;
• Participation in AI-supported interviews and conversations is completely voluntary and will not impact candidacy.