Airbnb Senior Staff Machine Learning Engineer – Guest & Host 

United States 

545387123

The Difference You Will Make:

There is huge potential for AI to how guests book and how Hosts manage listings at Airbnb. You will improve the Guest and Host experience by improving the intelligence of our product through state of the art machine learning models. This will involve close partnerships and influencing stakeholders across tech, product, and design organizations to identify, scope, and deliver AI features. While you will be leading efforts with XFN stakeholders supported by a team of engineers, you’ll also be working hand in hand with the team to prototype, evaluate, and implement models.

A Typical day:

Your contributions take a variety of shapes:

• Leverage structured and unstructured data to build and continuously improve state of the art Machine Learning models for Airbnb product, business and operational use cases.
• Work collaboratively with engineers, product managers, data scientists, and operations to identify problems and deploy ML capabilities.
• Stay up to date with SoT models and prototype machine learning product features and iterate with product and design.
• Drive best practices, develop playbooks, and create a repeatable process for shipping quality machine learning features.
• Hands-on development, productionization, and maintenance of machine learning models and pipelines at scale.

Your Expertise:

• Industry experience building and shipping end to end machine learning features
• 12+ years of industry experience in applied Machine Learning, with experience in both Natural Language Processing and Computer Vision
• A Bachelor’s, Master’s or PhD in CS/ML or related field
• Strong programming (Python / Java/ C++ or equivalent) skills with data and backend engineering expertise
• Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection) and algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization)
• Experience with technologies such as: Tensorflow, PyTorch, Kubernetes, Airflow (or equivalent), Kafka (or equivalent)
• Expertise with architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)

Offices: United States