AI Operations Specialist

Airbnb was born in 2007 when two Hosts welcomed three guests to their San Francisco home and has since grown to over 4 million Hosts who have welcomed more than 1 billion guest arrivals in more than 100, 000 cities and towns located in 220+ countries and regions across the globe. Every day, Hosts offer unique stays and one-of-a-kind activities that make it possible for guests to experience the world in a more authentic, connected way.

Airbnb was born with a creative spirit, and it’s one of the most defining parts of our culture. We use our curiosity and imagination to come up with unconventional solutions. These are the ideas at the core of Airbnb:

• Our community is based on connection and belonging.
• Our creativity allows us to imagine new possibilities for people.
• Our responsibility is to all of our stakeholders.
• It takes a unified team committed to our core values to embody these ideas, and our fast-moving team is committed to leading as a 21st century company.

We're the AI & ML Enablement team in Supply Tech. We help teams across Airbnb use AI to do real work, and prove that it works. Right now, we're helping business and field teams use AI to take on the review and quality work they run, and helping product teams understand whether an AI feature works before it reaches our users.

Day to day, we move manual review processes to AI and put people where their judgment matters most. We design new workflows so AI does the work from the start. We also build the labeled datasets that show whether the AI is right, and we keep everything running as models and policies change. Our methods and projects change as the models do, and we expect the scope of this role to grow with them.

You’ll work alongside our team on whichever problems AI could take on, whether that’s a business team with a process that's slow, expensive or inconsistent, or a product team getting an AI feature ready to launch. The job looks much the same either way. You start with how the work really gets done, help build an AI version of it, and test it until you trust the results.

Then you hand it off and use what worked on the next problem. The mix of projects will keep changing as AI takes on more of the work, so we care more about how you work than about a fixed list of projects. This is a hands‑on role. You help build the workflow, ship it, and stay on to make sure it works.

• Help the team decide which projects will drive the largest business impact, and make the case for the ones worth doing.
• Sit with the team that owns a problem, map how the work gets done today, and work out how AI can do it and where people step in for the hard cases.
• Build the AI workflow: write the prompts, set up the pipeline, and keep it at the agreed quality level, watching for drift once it's live.
• Build the test sets, measure accuracy, and calibrate LLM judges against human reviewers.
• Support product teams in agreeing on what "good enough to launch" means for an AI feature, and bring the evidence for the go/no-go call.
• Turn unclear policy into criteria a reviewer can apply consistently, working with product, Legal and Policy.
• Work with subject matter experts to write labeling guidelines and produce human-verified labels on real data.
• Run labeling at scale with vendor agents and check their work against agreement metrics and your own spot checks.
• Make what you build reusable and document what you learn, so the next team gets to production faster.
• Keep our methods current as models, tools and vendor agents change, and retire what stops working.
• Own the work streams you're assigned, from setup through quality checks. Model infrastructure and product roadmaps stay with the teams that own them.