Machine Learning Engineer

About Bumble Inc.

Bumble Inc. is an equal opportunity employer and we strongly encourage people of all ages, colour, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply. We’re happy to make any reasonable adjustments that will help you feel more confident throughout the process, please don’t hesitate to let us know how we can help. In your application, please feel free to note which pronouns you use (For example: she/her, he/him, they/them, etc).

Job Summary

As a Senior Machine Learning Engineer focused on scalability and productionisation, you will bring advanced machine learning models to life in production, from content understanding systems that interpret profiles, photos and text, to recommendation models that shape every match. You will build and scale the pipelines, infrastructure and automation that transform experimentation into reliable, high-impact features for our members.

Key Responsibilities

• Design, build and optimise ML pipelines and production systems that train, evaluate and serve recommendation models efficiently and at scale.
• Work in a cross-functional team alongside data scientists, machine learning scientists, software engineers and both technical and non-technical stakeholders.
• Partner with ML Scientists to translate research models into efficient, maintainable, and well-tested production systems.
• Implement monitoring, observability, and retraining strategies to ensure continuous model performance in a dynamic, global environment.
• Contribute to the evolution of our ML infrastructure, including CI/CD, model registries, and feature stores.
• Diagnose and resolve production ML issues, such as data inconsistencies and model drift, to identify and resolve infrastructure bottlenecks.
• Champion engineering best practices for scalability, reliability, and reproducibility across the ML lifecycle.

Requirements

• 2+ years of relevant industry experience.
• An advanced degree in Computer Science, Mathematics or a similar quantitative discipline.
• Strong software engineering background.
• You write clean, scalable, and maintainable code in Python or similar languages.
• Proven experience deploying and operating ML systems in production environments.
• Deep understanding of MLOps and infrastructure concepts: CI/CD for ML, feature stores, model serving, observability, and versioning.
• Experience with modern ML frameworks (e.g. PyTorch, TensorFlow) and orchestration tools (e.g. Airflow, Kubeflow, SageMaker, Ray).
• Familiarity with containerisation and cloud-native environments (e.g. Docker, Kubernetes, GCP/AWS).
• Skilled at debugging complex, distributed ML systems and optimising for performance at scale.
• Excellent communicator and collaborator.
• You communicate effectively with scientists, engineers, and non-technical stakeholders.