Airbnb Machine Learning Engineer Offline Risk 

United States 

700196629

A Typical Day:

As a machine learning engineer in Trust, your contributions will span a variety of shapes:

• Work with large-scale structured and unstructured data to build and continuously improve cutting-edge machine learning models for Airbnb’s product, business, and operational use cases. Some recent works include:
  • Supervised model ensembles that include multiple DNNs and graph-based models
  • Contextual Decision engine
  • Unsupervised clustering models for natural language understanding
  • LLM experimentation with label taxonomy and manual agent automation
  • Optimizing ranking algorithms for search
• Collaborate with a wide variety of business functions to predict and prevent physical safety and property damage incidents.
• Develop new holistic machine learning model detection strategies by partnering with other teams across the Trust Organization.
• Work collaboratively with cross-functional partners, including software engineers, data scientists, product managers, and operations to identify opportunities for business impact, and refine and prioritize requirements for fraud detection and mitigation.
• Hands-on develop, productionize, and operate machine learning models and pipelines at scale, including both batch and real-time use cases.
• Enhance and extend risk investigation tools to enable efficient decision-making on behaviors that could result in physical safety or property damage incidents.
• Create products to deter bad actors and restrict their usage on the platform.
• Provide and educate on guest and host safety standards to mitigate vulnerabilities.

Your Expertise:

• 2+ years of industry experience in applied machine learning, including a MS or PhD in relevant fields.
• A Bachelor’s, Master’s, or PhD in CS/ML or related field.
• Strong programming skills in Scala, Python, Java, C++, or equivalent languages, and data engineering skills.
• Strong understanding of machine learning best practices (e.g., training/serving skew minimization, A/B testing, feature engineering, feature/model selection), algorithms (e.g., neural networks/deep learning, gradient boosted trees, optimization), and domains (e.g., natural language processing, computer vision, personalization and recommendation, anomaly detection).
• Experience with two or more of these technologies: TensorFlow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouses (e.g., Hive).
• Industry experience building end-to-end machine learning infrastructure and/or building and productionizing machine learning models.
• Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).
• Experience with test-driven development, familiarity with A/B testing, incremental delivery, and deployment.
• Experience with the Trust and Risk domain is a plus.