The opportunity
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…
What you'll do
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.
New grad Ph.D in ML/AI or 2+ years of industry experience in applied ML/AI with a M.S. or B.S degree.
Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.
What they're looking for
- Deep understanding of Machine Learning best practices (e.g. training/serving: skew minimization, A/B test, feature engineering, feature/model selection), algorithms (e.g. neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection).
- Exposure to 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).
- Exposure to architectural patterns of large, high-scale software applications: (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).
- Proven ability to choose the right ML method to solve the problem within: current constraints while having a clear vision of the next iterations and a good balance between exploration and exploitation of different techniques.
