Airbnb is hiring a
Staff Machine Learning Engineer

Summary

Join Airbnb's Relevance team as a Senior Technical Individual Contributor to lead the architecture and design of the Relevance Intelligence Platform product, work on solving broader technical challenges, and contribute code as a Software Engineer.

Requirements

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience
• 15 years of technical experience in Applied Machine Learning inclusive MS or PhD in relevant fields, including producing code for a production system and shipping production systems
• Strong programming (Scala / Python / Java/ C++ or equivalent), data engineering skills and experience with 3 or more of these technologies: Tensorflow, PyTorch, Spark, Kafka
• Deep understanding of Machine Learning best practices (eg training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg natural language processing, computer vision, personalization and recommendation, anomaly detection)
• Experience with Machine Learning (AI/ML), including working with training models and developing existing model structures
• Experience with test driven development, familiar with A/B testing, incremental delivery and deployment

Responsibilities

• Define overall technical architecture for major parts of the Relevance Intelligent Platform and Product
• Architect large scale reliable Advanced AI/ML Based systems to support continuous growth of the business
• Influence the organization, engineering leaders, product managers, and the business to develop a unified approach to Intelligence Platforms at Airbnb
• 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
• Understand how key technical decisions will drive business outcomes
• Perform design and code reviews and advice/mentor other AI/ML engineers at Airbnb
• Champion the best AI/ML engineering practices and build the engineering excellence culture in the 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 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