About the job
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 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.
The Community You Will Join
The Relevance and Personalization team at Airbnb is responsible for search and personalization across the entire Airbnb digital platform. Be a leader in the team working on critical, impactful projects with focus on search ranking, recommendations, autocomplete, query understanding and Airbnb’s ranking platform. We collaborate closely with teams across Airbnb to customize the guest search experience and support a healthy marketplace for hosts and guests to further Airbnb’s mission of creating a world where people can Belong Anywhere. We work on all parts of the ML stack from analyzing search usage to running controlled experiments, building models, writing data pipelines, and implementing updates to the online search service
A Typical Day
- Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases.
- Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.
- Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
- Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.
- Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection.
Your Expertise
- 12+ years of industry experience in applied Machine Learning
- A Bachelor’s, Master’s or PhD in CS/ML or related field
- Strong programming (Scala / Python / Java/ C++ or equivalent) and data engineering skills
- Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive)
- Industry experience building end-to-end Machine Learning infrastructure and/or building and productionizing Machine Learning models
- Exposure to architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
- Experience with test driven development, familiar with A/B testing, incremental delivery and deployment.
- Ability to go deep and build the most impactful solutions while also leading multiple directions across multiple teams and organizations to ensure the success of our mission.
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