Senior Machine Learning Engineer, Customer Support Engineering

Airbnb Airbnb · Consumer · United States · Software Engineering

Senior Machine Learning Engineer at Airbnb focused on building and deploying agentic AI technologies for customer support, including chat and voice assistants. The role involves developing AI models, ML services, and leveraging techniques like SFT, RLHF, RAG, and LLM evaluation to create personalized and proactive customer experiences. Emphasis on shipping production-grade ML systems at scale.

What you'd actually do

  1. Champion the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems
  2. Work cross-functionally with product, design, and other engineering counterparts to design and build efficient AI solutions for Airbnb CS products
  3. Learn and share the latest AI/ML technologies with the team.

Skills

Required

  • PhD or Master's degree w/ 6+ YOE in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field — or equivalent industry experience
  • Hands-on expertise in LLM, including pretraining, fine-tuning (SFT, RLHF, GRPO), prompt engineering, RAG architectures, and LLM evaluation frameworks
  • Experience building Agentic AI systems — including multi-agent orchestration, tool-use, planning, memory, and autonomous reasoning pipelines (e.g., ReAct, LangGraph, AutoGen, or similar)
  • Experience of shipping production-grade ML/AI systems at scale, with deep understanding of ML infrastructure, model serving, and MLOps best practices
  • Excellent communication skills with the ability to collaborate effectively across Engineering, Product, and Design organizations

What the JD emphasized

  • Hands-on expertise in LLM, including pretraining, fine-tuning (SFT, RLHF, GRPO), prompt engineering, RAG architectures, and LLM evaluation frameworks
  • Experience building Agentic AI systems — including multi-agent orchestration, tool-use, planning, memory, and autonomous reasoning pipelines (e.g., ReAct, LangGraph, AutoGen, or similar)
  • Experience of shipping production-grade ML/AI systems at scale, with deep understanding of ML infrastructure, model serving, and MLOps best practices

Other signals

  • building agentic AI technologies
  • transforming customer service
  • early conceptual stages
  • inception to production