Machine Learning Engineer, Customer Support Engineering

Airbnb Airbnb · Consumer · United States · Software Engineering

Machine Learning Engineer role focused on adopting Agentic AI technologies for customer service at Airbnb. The role involves developing AI assistants (Chat, Voice), exploring SOTA Agentic architectures, enhancing AI models and ML services, and leveraging various AI techniques like SFT, RL, Distillation, RAG, LLM evaluation, and guardrails. The goal is to transform customer service with personalized, easy-to-use, and proactive experiences, shaping ideas from inception to production.

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/ 3+ 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

  • Developing Chat AI assistant, Voice AI Assistant
  • Adopting Agentic AI technologies
  • Leveraging SOTA Agentic architecture
  • Developing and enhancing AI models, ML services
  • Leveraging tools including SFT, Reinforcement learning, Distillation, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail