Senior Machine Learning Engineer, Relevance and Personalization

Airbnb is a global platform that connects hosts and guests, and they are seeking a Senior Machine Learning Engineer for their Relevance and Personalization team. The role involves building AI technologies for search ranking, collaborating with various teams, and developing machine learning models and pipelines.

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
• Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection

Requirements:

• 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
• 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. neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization)
• 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 applying large language models and modern NLP is a plus, e.g. for sequence tagging, text generation, intent classification, or representation learning
• Familiarity with building natural-language, AI-native search experiences is a plus, e.g. autocomplete/smart compose, query understanding, or user-intent modeling