Airbnb Staff Machine Learning Engineer Payments 

United States 

643448791

The Difference You Will Make:

As a Staff Machine Learning Engineer, your expertise will be pivotal in developing cutting-edge Machine learning solutions including generative AI to define and shape the future of the Airbnb Payments experience. you will also partner with product managers, software engineers, and operation teams to leverage engineering innovations to simplify the business requirements into scalable solutions.

A Typical Day:

• Envision, champion, and support the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems.
• 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.
• Lead, mentor, challenge and grow enthusiastic, collaborative AI/ML culture within the organization

Your Expertise:

• 9+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.
• Strong programming (Python/Java) 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, personalization and recommendation).
• 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 large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).
• Prior knowledge of ML Applications in the Payments domain is highly desirable