Airbnb Staff Machine Learning Engineer Core 

United States 

537987095

As a staff machine learning engineer, your expertise will be pivotal in developing Conversational AI solutions and other cutting-edge Machine learning techniques to define and shape the future of the Airbnb Community Support experience. You will also partner with product managers, software engineers, and operation teams to leverage engineering innovations to simplify the business requirements into scalable solutions.

• Design, develop, productionize and operate Machine learning models, including Large-Language-Models, and pipelines at scale, for both batch and real-time use cases.
• Collaborate with machine learning infrastructure engineering teams to evolve how we build reusable and scalable AI solutions for Airbnb products.
• 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.
• 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.
• Work collaboratively with cross-functional partners including product managers, operations and data scientists, identify opportunities for business impact, understand and prioritize requirements for machine learning systems and data pipelines, drive engineering decisions and quantify impact.

• 9+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
• Strong programming (Python/Java) 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. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg. natural language processing, personalization and recommendation)
• 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.