Bumble Inc. is looking to 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. The company aims to build and scale the pipelines, infrastructure and automation that transform experimentation into reliable, high-impact features for its members.
Requirements
- 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.
- Interested in contributing to the responsible development of ML and AI, with a focus on building systems that are fair, equitable and accountable.
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.
Other
- 2+ years of relevant industry experience.
- An advanced degree in Computer Science, Mathematics or a similar quantitative discipline.
- Excellent communicator and collaborator. You communicate effectively with scientists, engineers, and non-technical stakeholders.
- This role is based in Austin, and we ask that you’re within a commutable distance to this office, so that you’re able to come onsite regularly to collaborate across engineering teams.
- We have a hybrid environment that requires you to be in the office Monday - Wednesday.
- Please note: We are unable to offer Visa sponsorship at this time
