Lead ML Engineer: Matching & Personalization

Bumble Inc.; New York (NY); On-site; USD 345,000 - 410,000; Full time

Job summary

Bumble Inc. is seeking a Principal Machine Learning Engineer, Matching & Recommendations to define the ML strategy behind Bumble’s recommendation systems.

You’ll remain hands-on while tackling retrieval, ranking, personalization, experimentation, and two-sided marketplace optimization across cross-functional teams. You’ll mentor senior contributors, design scalable data and ML pipelines with Spark/Airflow, deploy PyTorch models and AI Agents, and champion responsible AI practices to improve user

Qualifications

• Deep expertise in machine learning with hands-on experience in production systems.
• Ability to mentor senior contributors and influence technical direction.
• Experience designing scalable data and ML pipelines using Spark or Airflow.
• Experience with production AI agents and LLMs is a plus.

Responsibilities

• Define and lead the technical strategy for ML systems powering recommendations, ranking, and personalization.
• Design, develop, and deploy production-grade models using PyTorch in high-traffic environments.
• Build and deploy AI Agents with foundation models and MCP integrations.
• Architect end-to-end ML pipelines integrating data processing with training, evaluation, and deployment.
• Drive experimentation frameworks including A/B testing and offline evaluation.

Skills

ML strategy

Mentoring

Cross-functional collaboration

Senior IC

Tools

PyTorch

TensorFlow

Spark

Airflow

Job description

Bumble Inc. is seeking a Principal Machine Learning Engineer, Matching & Recommendations to define the ML strategy behind Bumble’s recommendation systems.

You’ll remain hands-on while tackling retrieval, ranking, personalization, experimentation, and two-sided marketplace optimization across cross-functional teams. You’ll mentor senior contributors, design scalable data and ML pipelines with Spark/Airflow, deploy PyTorch models and AI Agents, and champion responsible AI practices to improve user