Staff Machine Learning Engineer, Community Support Engineering
at Airbnb
USD 212,000-260,000 per year
Used Tools & Technologies
GenAIRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Machine Learning @ 4
Communication @ 4
Customer Support @ 4
LLM @ 4
Generative AI @ 4
AI @ 4
Agentic AI @ 1
RAG @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Airbnb's Community Support Products (CSP) Machine Learning team drives CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting generative AI technologies to enable an intelligent, scalable, and exceptional service experience. The team develops and enhances AI models, ML services and tools including LLM fine-tuning and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning, and guardrail systems for a wide range of applications across Airbnb.
Responsibilities
- Envision, champion, and support 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 customer support products.
- Build and leverage cutting-edge AI technologies to transform Airbnb’s customer service by delivering personalized, easy-to-use, and proactive experiences.
- Shape initiatives from inception to production, turning conceptual ideas into production systems.
- Learn and share the latest AI/ML technologies with the team.
Requirements
- PhD or Master’s degree (preferably in Computer Science) or equivalent experience.
- 9+ years of machine learning engineering experience, with ownership responsibility over large-scale software systems.
- Background in the design and development of AI and ML systems and services, with a deep passion for building efficient and scalable ML-powered products.
- Experience with LLM fine-tuning and optimization, retrieval-augmented generation (RAG)/search, LLM evaluation and testing automation, feedback-based learning, and guardrail/safety systems.
- Experience with LLM-driven chatbots and agentic AI products is a strong plus.
- Excellent communication skills and ability to work well within and across engineering, product, and design teams.
Location & Work Model
- This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed with your manager.
- You must live in a U.S. state where Airbnb, Inc. has a registered entity (some states may be excluded); recruiters will confirm eligibility based on employing entity.
Compensation & Offices
- Base pay range: $212,000—$260,000 USD. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.
- Offices: United States
Inclusion & Accessibility
- Airbnb is committed to inclusion and belonging and encourages all qualified individuals to apply.
- Candidates with disabilities who require reasonable accommodation for the application or interview process are instructed to contact [email protected] with their full name, the role, and the accommodation needed.