airbnb
Software Engineer, Community Support Engineering
About this role
Airbnb seeks a Backend Software Engineer to join the Community Support team's Ambassador Routing & Channels (ARC) group, building intelligent routing infrastructure that matches support requests to the right expert across channels. You'll own core routing systems, decision engines, and workflow orchestration while collaborating cross-functionally to deliver scalable customer support experiences for millions of daily interactions.
What you'll do
- Design and maintain core routing infrastructure including decision engines, workflow orchestration, and rule management systems
- Lead engineering initiatives on intelligent agent matching and personalized support for high-value customers
- Consolidate fragmented routing logic across platforms into a centralized, auditable system
- Collaborate with Product, Operations, Data Science, and partner teams on routing improvements
- Participate in on-call rotations, incident response, and reliability optimization for production systems
- Mentor junior engineers and contribute to engineering excellence through design and code reviews
What they're looking for
- Backend development and service-oriented architectures (3+ years)
- Workflow optimization and scalable system design
- AI/LLM prompt engineering and fine-tuning
- RAG patterns, memory routing, and agent planning
- System resilience, performance optimization, and availability
- Cross-functional collaboration and communication
- Agile development practices
- Technical design and code quality standards
Opens the official application on the employer’s site. No login required.
airbnb
Airbnb builds hospitality and travel experiences, supported by advanced technology infrastructure. The company is hiring ML engineers to develop AI-powered customer service systems and audiovisual infrastructure engineers to manage large-scale AV systems across global offices.
- Website
- airbnb.com
Likely interview questions
- Describe your experience designing and maintaining routing or decision engine systems at scale. What challenges did you encounter?
- How have you approached prompt engineering or optimization for AI/LLM-driven systems in production?