Bumble Inc. is looking to evolve its ML architecture and platform to ensure its machine learning systems are fast, robust, and responsible, and to turn research into resilient production systems that deliver measurable impact for its members.
Requirements
- Strong software engineering background. You write clean, scalable, and maintainable code in Python or similar languages.
- Deep expertise in building, deploying, and scaling production ML systems at large scale.
- Proven ability to define and lead technical strategy or architecture for complex, distributed ML platforms or pipelines.
- Experience with production-grade ML frameworks (e.g. PyTorch, TensorFlow) and orchestration tools (e.g. Airflow, Kubeflow, Ray, or SageMaker).
- Proficiency with cloud-native environments and containerised workloads (e.g. Docker, Kubernetes, GCP/AWS).
- Deep understanding of MLOps, observability, and model lifecycle management.
- Track record of mentoring engineers and influencing engineering practices across teams.
Responsibilities
- Lead the technical strategy and architectural evolution of Bumble’s ML recommendation and content understanding systems.
- Design and guide the development of scalable pipelines and serving systems that support pre-trained, fine-tuned, and in-house models at high throughput.
- Define and champion best practices for reliability, observability, and retraining across the ML lifecycle.
- Collaborate with ML Scientists to bring cutting-edge research into production, improving model performance and iteration velocity.
- Mentor and support other Machine Learning Engineers and Scientists, helping raise the bar for engineering excellence and technical decision-making.
- Drive cross-functional technical initiatives across Recommendations, Platform, and other product areas.
- Diagnose and resolve complex production challenges across data, infrastructure, and model systems, ensuring the long-term health and scalability of our ML ecosystem.
Other
- Typically 8+ years of professional experience building and operating machine learning systems.
- An advanced degree in Computer Science, Mathematics or a similar quantitative discipline.
- Excellent communicator who can translate between technical detail and business impact.
- Passionate about responsible ML — fairness, transparency, and reliability in real-world systems.
- We have a hybrid environment that requires you to be in the office Monday - Wednesday.
