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Bumble is building the Love Company, a platform for healthy, equitable relationships and friendships. Machine Learning powers the matching, recommendation, and personalization experiences that help members discover meaningful connections.
As a Machine Learning Engineer on the Matching & Recommendations team, you will develop and improve ML systems end-to-end. You'll own well-defined ML problems from data exploration and feature engineering through model training, evaluation, production deployment, and monitoring. You'll work hands-on with experienced engineers while contributing to the evolution of Bumble's recommendation platform.
Key responsibilities include:
- Explore, develop, and deliver modern ML solutions that improve recommendations, matching, and personalization across the platform
- Own defined ML problems end-to-end, from data exploration through production deployment and iteration
- Use modern ML frameworks (PyTorch, TensorFlow) to design, train, and optimize models for production
- Contribute to experimentation, including A/B testing and offline evaluation, using results to improve performance
- Build, maintain, and monitor production models at scale, diagnosing and resolving issues
- Write high-quality, maintainable code and participate in code reviews and technical discussions
- Apply responsible AI practices, considering fairness, transparency, privacy, and member safety
- Collaborate with ML, Engineering, Product, and Data teams to translate problems into practical ML solutions
Requirements:
- 3+ years of hands-on experience building and shipping ML models in production (or equivalent demonstrated skills)
- Strong Python programming skills and proficiency with PyTorch or TensorFlow
- Industry experience researching or applying ML, ideally in recommendation systems, ranking, retrieval, search, or personalization
- Good understanding of the ML development lifecycle (data, feature development, training, evaluation, deployment, monitoring, iteration)
- Understanding of MLOps and infrastructure concepts (CI/CD for ML, feature stores, model serving, observability, versioning)
- Familiarity with containerization and cloud-native environments (Docker, Kubernetes, GCP, or comparable)
- Familiarity with experimentation methodologies (A/B testing, offline evaluation, model performance metrics)
- Strong problem-solving skills and ability to navigate technically ambiguous problems independently
- Agile mindset, adapting based on data and experimentation while maintaining focus on outcomes
- Growing AI fluency, with ability to apply ML techniques and emerging technologies responsibly
Bonus experience:
- Practical production experience building recommendation systems, ranking, retrieval, search, or personalization systems
- Modern recommendation approaches (embeddings, two-tower models, learning-to-rank, representation learning)
- Modern ML architectures (transformers, graph neural networks, contrastive learning, multimodal embeddings)
- Real-time or low-latency ML inference systems at scale
