About the position
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. At Airbnb, our mission is to create a world where anyone can belong anywhere. We use Data and Machine Learning extensively to create a more connected, empowered, and safer global community and enable an intelligent & worry-free travel experience. ML Infrastructure, which is the team you will join in, is tasked to provide common shared foundations for modeling, data, governance and productivity to ensure Airbnb’s AI/ML models and applications are built with the highest standards in the industry.
Responsibilities
- Design, build, automate, and maintain robust, scalable data pipelines using SparkSQL, Scala, and Airflow.
- Develop and optimize data models ensuring high-quality, consistent, and accurate data to support broad AI/ML product feature decisions.
- Collaborate closely with peer ML Infra teams to deliver automated data solutions driving AI/ML acceleration.
- Contribute to scalable GenAI infrastructure by leveraging foundational language and vision models to create high quality datasets that power cutting edge GenAI applications.
- Partner with key customer teams to deliver high-impact, high-quality datasets core to Airbnb's roadmap.
- Utilize leading open-source technologies including Spark, Airflow, Ray, MLFlow, TensorFlow, PyTorch, Docker, Kubernetes, and more.
Requirements
- 5+ years of relevant industry experience (BS/Masters) or 2+ years with a PhD.
- Strong coding skills in Python, Java, or equivalent languages.
- Hands-on experience with distributed processing technologies (Spark, Kafka, Flink, Hadoop) and distributed storage (HDFS, S3).
- Solid knowledge of data warehousing concepts and databases (e.g. PostgreSQL, MySQL, Redshift, BigQuery, ClickHouse).
- Expertise building scalable ETL pipelines using schedulers like Airflow, Luigi, Oozie, or AWS Glue.
- Proven ability to analyze large datasets, identify insights, and drive impactful product solutions.
- Excellent written and verbal communication skills; comfortable collaborating cross-functionally.
- Experience building end-to-end Machine Learning platforms and deploying ML models.
- Familiarity with Kubernetes, Docker, and modern infrastructure tools.
- Deep understanding of distributed systems and engineering best practices.
Benefits
- Base pay range of $191,000—$223,000 USD.
- Bonus eligibility.
- Equity options.
- Employee Travel Credits.
