Airbnb
Consumer · Travel marketplace
- HQ
- San Francisco, US
- Founded
- 2008
- Size
- 7,000+
- Funding
- Public (ABNB)
- Website
- airbnb.com
- Blog
- medium.com
Products
Consumer · Travel marketplace
Currently tracking 22 active AI roles, with 106 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $117k–$310k (avg $214k).
Airbnb currently has 36 active job listings related to Artificial Intelligence. The majority of these roles, 64%, are focused on the agents stage, with application stage roles making up another 22%. The top function for these positions is Engineering, followed by Product. Frequent tech tags include agent_orchestration, llm_observability, and model_serving, suggesting a focus on building and managing AI systems.
Airbnb currently has 40 active AI-related roles in our index. The most common open titles are: (Contract) Senior Data Scientist, Platform Inference - MarTech DS Measurement, Lead Analyst, Advanced Analytics, Lead, Advanced Analytics, Hosting Services (Brazil) , Lead, Advanced Analytics, Hosting Services (Mexico), Machine Learning Engineer, Community Support Engineering. Most positions are in Engineering and Product.
Airbnb's active AI hiring is concentrated in: agents (60%), application (28%), evaluation (5%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Airbnb is hiring AI talent in: United States (29 roles), China (4 roles), Brazil (4 roles), India (3 roles).
Job postings at Airbnb most frequently mention: Machine Learning, System Design, Market & Technology Trend Analysis, Statistics, Large Language Models (LLMs).
In the past 30 days, Airbnb has posted 19 new AI-related roles. That is a +36% change versus the prior 30 days (14 → 19).
| Title | Stage | AI score |
|---|---|---|
| Principal AI/ML Researcher / Engineer Reasoning, Planning, and Decision-making systems This role focuses on researching and engineering advanced AI systems for reasoning, planning, and decision-making within a consumer platform. It involves architecting post-training intelligence frameworks, integrating Large Reasoning Models (LRMs) with Knowledge Graphs, and applying Reinforcement Learning (RL) for adaptive planning and control. The core of the role is to build cognitive AI systems that combine foundational models, memory, and recursive planning strategies, with a significant emphasis on designing and deploying multi-agent systems for distributed intelligence and complex coordination. The ultimate goal is to transform the platform from prediction-based to one capable of deliberation, foresight, and agency, impacting various user and internal workflows. | AgentPost-train | 9 |
| Principal Engineer -In Bayesian, Large Foundational Systems, and Distributional Reinforcement Learning Lead advanced research and development of cutting-edge AI models integrating Bayesian frameworks with advanced architectures, including Mixture of Models, multi-pass sharded systems, multitask and multi-objective optimization, and external knowledge incorporation. Innovate ways to interoperate and integrate LLMs and LMMs with Reasoning, Planning, and Decisioning abilities into Bayesian frameworks to create a seamless foundational model fabric. Ensure these models perform efficiently at scale and integrate into live systems impacting product and user experience. The role aims to transform Airbnb's AI stack into probabilistic, adaptive, uncertainty-aware intelligence systems capable of reasoning under ambiguity and continuously learning. | AgentPost-train | 9 |
| Data Science Intern Airbnb is seeking a PhD Data Science Intern to join the Core Data Science team for their Summer Intern Program. The intern will work on the Trip Intelligence working group, focusing on improving the Guest satisfaction (GSAT) model by applying AI to various data sources. Key responsibilities include conducting literature reviews on active learning, investigating innovative approaches to labeling training and evaluation datasets, implementing an active learning pipeline, and exploring LLM labeling agents. | Post-trainData | 7 |