Senior Machine Learning Engineer, Relevance and Personalization

Job Summary

The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. This role involves developing end-to-end ranking algorithms and ecosystems for optimizing multiple critical business objectives.

Location

This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states.

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.
• Collaborate 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.
• 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.

Qualifications

• 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.
• 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.