Description
We're looking for a Staff Machine Learning Engineer to join our Listings and Host Tools Data and AI team. As a member of this team, you will support host personalization products and provide data-driven solutions to achieve a superior host experience on Airbnb.
The Listings and Host Tools Data and AI team owns data pipelines and ML models and builds services for serving that are used in the above areas. We leverage open source, third-party, and homegrown ML models to improve the Host and Guest experience.
As an ML engineer, you will partner closely with our data science, product partners, and other ML + data engineers on the team to execute on these opportunities in order to improve the Host and Guest product experience on Airbnb.
Your responsibilities will include:
- Working with large-scale structured and unstructured data to build and continuously improve cutting-edge Machine Learning models for Airbnb product, business, and operational use cases.
- Collaborating with cross-functional partners, including software engineers, product managers, operations, and data scientists, to identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.
- Prototyping machine learning use cases for use in the product and working with stakeholders to iterate on requirements.
- Developing, productionizing, and operating Machine Learning models and pipelines at scale, including both batch and real-time use cases.
- Designing and building services and APIs to enable serving ML model-driven data to product use cases.
We're looking for someone with 8+ years of industry experience in applied Machine Learning, including a Master's or Ph.D. in a relevant field. You should have experience in both Natural Language Processing and Computer Vision, as well as strong programming and data engineering skills.
You should also have a deep understanding of Machine Learning best practices, algorithms, and domains, as well as experience with technologies such as TensorFlow, PyTorch, Kubernetes, Spark, Airflow, and data warehouses.
If you're passionate about building end-to-end Machine Learning infrastructure and productionizing Machine Learning models, we'd love to hear from you!