Key skills
Machine LearningPythonData EngineeringTensorflowDeep LearningKubernetesSparkA/B TestingPrototypingCollaborationJavaC++CScalaMLNLPNatural Language ProcessingComputer VisionPyTorchTensorFlowAirflow
About this role
Airbnb is a global platform that connects hosts and guests for unique stays and experiences. They are seeking a Senior Machine Learning Engineer to improve the Host and Guest product experience by leveraging machine learning models and collaborating with cross-functional teams.
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 (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization, state-of-art NLP and CV algorithms) and domains (eg. natural language processing, computer vision, personalization and recommendation, anomaly detection)
- 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, as well as integrating to product use cases
- 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