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Bumble is building the Love Company, a platform where healthy, equitable relationships and friendships can start and grow. Machine Learning sits at the heart of this mission, powering matching, recommendation, and personalization experiences that bring people together.
As Principal Machine Learning Engineer for Matching & Recommendations, you will define the technical and ML strategy behind the next generation of Bumble's recommendation systems. You'll remain hands-on while tackling complex problems across retrieval, ranking, personalization, experimentation, and two-sided marketplace optimization. You'll partner across Engineering, Product, Data Science, and Analytics to translate ambiguous member problems into scalable ML solutions, lead through technical influence, raise the bar for ML engineering across the organization, and help build systems that optimize for better connections and meaningful member outcomes.
Key responsibilities include:
- Define and lead technical strategy for AI/ML systems powering recommendations, matching, ranking, and personalization, delivering measurable improvements in member outcomes, engagement, and safety
- Design, develop, and deploy production-grade models using modern ML frameworks like PyTorch, ensuring scalability, reliability, and performance in high-traffic environments
- Build and deploy production AI agents using foundation models and fine-tuned LLMs, alongside sub-agents, tools, and MCP integrations
- Architect end-to-end ML pipelines integrating large-scale data processing (Spark, Airflow) with model training, evaluation, deployment, and monitoring
- Define and evolve experimentation strategies including A/B testing, offline evaluation, and online measurement to continuously improve model performance
- Partner cross-functionally with Product, Engineering, and Data leadership to translate business and member challenges into impactful ML solutions
- Mentor and elevate senior individual contributors, fostering a culture of excellence, curiosity, and continuous learning
- Take ownership of complex, ambiguous problem spaces from insight to measurable impact
- Champion responsible AI practices, ensuring fairness, transparency, privacy, and member safety throughout ML system design and operation
Requirements:
- Typically 10–15 years of relevant experience (alternative backgrounds demonstrating equivalent skills, scope, and impact welcome)
- Deep expertise in Machine Learning with significant hands-on experience designing, building, and deploying large-scale ML systems in production
- Strong proficiency in Python and at least one major ML framework (PyTorch or TensorFlow)
- Experience in recommendation systems, ranking, retrieval, personalization, or NLP
- Deep understanding of modern recommendation-system approaches and ability to reason about model architecture, features, loss functions, evaluation methodology, experimentation, and trade-offs between offline and online performance
- Proven experience prompting and fine-tuning LLMs and building production AI agents using modern agentic architectures and tooling
- Demonstrated ability designing scalable data and ML pipelines using Spark, Airflow, or comparable distributed systems
- Proven track record operating as senior individual contributor, setting technical direction and influencing strategy across teams without direct authority
- Comfort navigating highly ambiguous technical and product problems, balancing immediate delivery with longer-term architectural and ML strategy
- Effective cross-functional partnership and collaboration with purpose, taking ownership of outcomes in complex organizational environments
- Track record mentoring and developing other engineers, role-modeling respect and excellence while building inclusive, high-performing teams
- Strong AI fluency and technical judgment, ability to independently design, evaluate, and optimize ML systems while guiding others in responsible AI application
