Lead ML Architect for Matching & Personalization

Bumble Inc.; Austin (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Bumble Inc. is seeking a Principal Machine Learning Engineer, Matching & Recommendations, to shape the ML strategy and own the next generation of recommendation systems.

You will stay hands-on across retrieval, ranking, personalization, experimentation, and two-sided marketplace optimisation, collaborating with Engineering, Product, Data Science, and Analytics to convert complex member problems into scalable ML solutions.

Qualifications

• 10+ years of hands-on ML experience preferred.
• Expertise in building and deploying large-scale systems in production.
• Strong Python proficiency and ML framework experience (PyTorch, TensorFlow).
• Experience with prompting/fine-tuning large language models (LLMs) and AI agents.

Responsibilities

• Define and lead ML strategy for recommendations, ranking, and personalization.
• Design, develop, and deploy production-grade models using PyTorch or similar frameworks.
• Build AI Agents with LLMs, sub-agents, tools, and MCP integrations.
• Architect end-to-end ML pipelines integrating data processing with training and deployment.
• Run experimentation frameworks including A/B testing and offline evaluation.
• Partner with Product, Engineering, and Data leadership to translate business problems into ML solutions.
• Mentor senior contributors and elevate the ML culture across the org.
• Take ownership of ambiguous problems and drive initiatives from insight to impact.

Skills

Machine Learning

Python

PyTorch

LLMs

Spark

Airflow

Education

Advanced degree in CS/ML (MS/PhD)

Tools

Spark

Airflow

Kubernetes

Job description

Bumble Inc. is seeking a Principal Machine Learning Engineer, Matching & Recommendations, to shape the ML strategy and own the next generation of recommendation systems.

You will stay hands-on across retrieval, ranking, personalization, experimentation, and two-sided marketplace optimisation, collaborating with Engineering, Product, Data Science, and Analytics to convert complex member problems into scalable ML solutions.