Machine Learning Engineer, Relevance and Personalization

The Community You Will Join:

The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. Be a leader in the team working on critical, impactful projects with focus on developing end-to-end ranking algorithms and ecosystems for optimizing multiple critical business objectives.

The Difference You Will Make:

We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) at Airbnb. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb’s mission of creating a world where people can Belong Anywhere.

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.
• 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.

Your Expertise:

• 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.
• 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