Machine Learning Engineer, Relevance and Personalization

Airbnb is a leading company in the travel and hospitality industry, and they are seeking a Machine Learning Engineer for their Relevance and Personalization team. The role involves developing end-to-end ranking algorithms and optimizing search and recommendation systems to enhance user experience on the Airbnb platform.

Responsibilities:

• 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

Requirements:

• 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 good balance between exploration and exploitation of different techniques
• Ability to go deep and build the most impactful solutions while also leading multiple directions across multiple teams and organizations to ensure the success of our mission