Airbnb Staff Machine Learning Engineer Trust Screenings 

United States 

474775944

The Difference You Will Make:

As a Staff Machine Learning Engineer on the Trust Screenings team, you will be working with product managers, data scientists, designers, and customer service operations to innovate new ways to predict the physical safety and property damage incidents on the platform.

Problems will be so vague and ambiguous hence you must have the curiosity to dig deep into various patterns/behaviors of the users to understand how and where these life safety incidents may occur.

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 physical safety and property damage incidents in real time.
• Creating new holistic machine learning model detection strategies by collaborating with other trust and safety 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.

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.