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SeniorFull-timePythonJavaC++KubernetesMachine LearningData ScienceTensorFlowPyTorchAIAPI
Salary Range
$132,600 - $179,400 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
The Supply & Insights Data and AI team at Airbnb is seeking a Machine Learning Engineer to join their team. This role involves leveraging open source, third party, and home-grown ML models to improve the Host and Guest experience on Airbnb.
Key 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, Data Scientists, and Operations, to identify opportunities for business impact.
- →Understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.
- →Prototype machine learning use cases for use in the product, and work with stakeholders to iterate on requirements.
- →Develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
- →Design and build services, API to enable serving ML model driven data to product use cases.
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, algorithms, and domains
- ✓Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (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
- ✓Experience with test driven development, familiar with A/B testing, incremental delivery and deployment.
