Machine Learning Engineer, Community Support Engineering
The vacancy is well-structured and informative, providing clear expectations and compensation details.
Check Match — Just drop your CV
See your fit for Machine Learning Engineer, Community Support Engineering in seconds.
Overview
Join Airbnb as a Machine Learning Engineer to develop cutting-edge AI technologies for enhancing customer service experiences. Collaborate across teams to build efficient AI solutions and shape innovative concepts into impactful realities. Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.
A Typical Day
- •Champion the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems.
- •Work cross-functionally with product, design, and other engineering counterparts to design and build efficient AI solutions for Airbnb CS products.
- •Learn and share the latest AI/ML technologies with the team.
How We'll Take Care of You
- •Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands.
- •The base pay range is subject to change and may be modified in the future.
- •This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.
- •Pay Range $170,000 — $180,000 USD.
Your Expertise
- •(Required) PhD or 3+ YOE in Computer Science, Machine Learning, Statistics, 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.