Airbnb Staff Machine Learning Engineer Account Integrity 

United States 

854936676

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.
• Working together with a wide variety of business functions to stop fraud attacks in real time.
• Creating new holistic machine learning model detection strategies by collaborating with other fraud prevention teams around the Trust Organization.
• 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 fraud detection and mitigation.
• Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
• Examples include: ML models to detect Fake Account creation attempts and ATO attempts.

Your Expertise:

• 8+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
• 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
• Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization and recommendation, anomaly detection)
• 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.
• Experience with the Trust and Risk domain is a plus.