Principal Machine Learning Engineer, Matching & Recommendations

Overview

As Principal ML Engineer for Matching & Recommendations, you define and lead the ML strategy powering Bumble’s recommendation, matching and personalization systems. You work hands-on to build scalable production models, AI agents and end-to-end pipelines, shaping how members connect across Bumble’s products. You collaborate with cross-functional partners to translate complex problems into impactful ML solutions, and you champion responsible AI practices. This role offers the chance to influence company-wide ML direction while delivering measurable improvements in member outcomes and safety.

Responsibilities

• Define and lead the ML/AI strategy for recommendations, ranking and personalization across Bumble products
• Design, develop and deploy production-grade models using PyTorch (scalability, reliability in high-traffic environments)
• Build and deploy AI agents with foundation models, LLMs and MCP integrations
• Architect end-to-end ML pipelines integrating Spark and Airflow for training, evaluation, deployment and monitoring
• Define experimentation approaches (A/B testing, offline/online evaluation) to improve model performance and outcomes
• Collaborate with Product, Engineering and Data leadership to translate business problems into ML solutions
• Mentor senior engineers and foster a culture of Excellence, Curiosity and learning across the ML community
• Tackle complex, ambiguous problems from insight to measurable impact with adaptable strategies
• Champion responsible AI, ensuring fairness, privacy and member safety in design and operation

Key requirements

• 10–15 years of relevant experience (welcome alternative backgrounds)
• Deep ML expertise with hands-on experience deploying large-scale ML systems
• Strong Python and proficiency in PyTorch or TensorFlow; experience in recommender systems, ranking, retrieval, personalization or NLP
• Understanding of modern recommendation-system approaches and trade-offs between offline and online outcomes
• Experience prompting and fine-tuning LLMs and building production AI agents
• Experience with scalable data and ML pipelines using Spark, Airflow or similar systems
• Ability to operate as a senior individual contributor, influencing strategy without direct authority
• Comfort with ambiguous problems and balancing short-term delivery with long-term strategy
• Proven track record mentoring engineers and building inclusive teams
• Strong AI fluency, technical judgment and guidance on responsible AI

• cross-functional collaboration
• leadership and mentoring
• ability to navigate ambiguity
• Python
• PyTorch
• TensorFlow

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