Principal Machine Learning Engineer, Matching & Recommendations

Overview

As Principal ML Engineer, you will shape the ML strategy for Bumble’s recommendations, matching and personalisation. You’ll work across retrieval, ranking, and personalization to improve member outcomes at scale, partnering with cross-functional teams. You’ll lead by technical influence, build production-grade models, and mentor engineers while advancing responsible AI practices. This is a chance to influence how AI drives meaningful connections at a global dating and friendship platform.

Responsibilities

• Define and lead the technical strategy for AI/ML systems powering recommendations, matching, ranking and personalisation across Bumble products
• Design, develop and deploy production-grade models using PyTorch for high-traffic environments
• Build and deploy production AI agents using foundation models and fine-tuned LLMs, plus sub-agents and MCP integrations
• Architect end-to-end ML pipelines integrating Spark and Airflow with training, evaluation, deployment and monitoring
• Define and evolve experimentation strategies including A/B testing, offline evaluation and online measurement
• Partner with Product, Engineering and Data leadership to translate business problems into ML solutions
• Mentor senior engineers, fostering Excellence and continuous learning across Bumble’s ML community
• Own complex problem spaces from insight to measurable impact while adapting approaches as needed
• Champion responsible AI practices around fairness, transparency, privacy and safety

Key requirements

• 10–15 years of relevant experience (or equivalent skills and impact)
• Deep expertise in ML with hands-on experience in production-scale systems
• Strong Python proficiency and experience with ML frameworks (PyTorch or TensorFlow)
• Experience with recommendation systems, ranking, retrieval, personalization or NLP
• Experience prompting and fine-tuning LLMs and building production AI agents
• Experience designing scalable data/ML pipelines using Spark, Airflow or similar
• Ability to influence strategy across teams without direct authority
• Comfort with ambiguous technical/product problems and balancing delivery with architecture
• Proven mentoring experience and building inclusive, high-performing teams
• Strong AI fluency and judgement for responsible AI applications
• leadership
• cross-functional collaboration
• mentoring
• PyTorch
• TensorFlow
• Recommender systems

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