Lead Machine Learning Engineer at Bumble shaping user connections through machine learning. Driving end - to - end AI solutions while mentoring engineers in a hybrid work environment.
About the role
- Senior Machine Learning Engineer at Bumble developing scalable AI systems for personalized user interactions. Leading machine learning model development and deployment from exploration to production.
Responsibilities
- Build and deploy machine learning models that improve recommendations, ranking, and personalization, driving measurable impact on user experience and engagement
- Own problems end-to-end, from data exploration and feature engineering through to model training, evaluation, and production deployment
- Develop and maintain scalable ML pipelines using tools such as Spark and Airflow to support reliable, high-quality model delivery
- Apply modern ML frameworks (e.g. PyTorch or TensorFlow) to design, train, and optimise models in production environments
- Contribute to experimentation frameworks, including A/B testing and offline evaluation, to iterate on model performance with an agile mindset
- Collaborate cross-functionally with Product and Engineering, working with purpose to translate product questions into ML solutions
- Take ownership of delivering high-quality solutions and see work through from insight to impact, balancing speed and rigor
- Apply responsible AI practices, ensuring fairness, transparency, and safety are considered in model development and deployment
Requirements
- Typically requires 5–8 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.
- Strong experience building and deploying machine learning models in production environments
- Proficiency in Python and experience with at least one major ML framework (e.g. PyTorch, TensorFlow)
- Experience working with data pipelines and distributed systems (e.g. Spark, Airflow) to support ML workflows
- Familiarity with experimentation methodologies such as A/B testing and model evaluation techniques
- Ability to collaborate effectively across functions, demonstrating strong ownership and a collaborative mindset
- Demonstrates an agile mindset, adapting approaches based on data and evolving priorities while maintaining focus on outcomes
- Growing AI fluency, with the ability to independently apply ML techniques and emerging tools (including LLMs) to solve problems responsibly
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