Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. It takes a unified team committed to our core values to achieve this goal. Airbnb's various functions embody the company's innovative spirit and our fast-moving team is committed to leading as a 21st century company.

Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. It takes a unified team committed to our core values to achieve this goal. Airbnb's various functions embody the company's innovative spirit and our fast-moving team is committed to leading as a 21st century company.

What is Payments Risk at Airbnb? 

Payments at Airbnb allows any two people in the world to frictionlessly exchange money with easy to use payments services. It is a core strategy to fulfill Airbnb’s belongs anywhere mission. We are building a world-class payments platform that moves billions of dollars, in 191 countries, with 75 currencies, through a complex ecosystem of payments partners. We build and maintain our own in-house global payments platform because no solution exists with the global reach needed. As the platform grows we’ll be adding new payment partners, global licenses, compliance and regulation controls, and building new payment experiences for our guests and hosts.

 

Payments Risk is a cross-functional Payments org with the mission to build a world-class payments risk detection, mitigation and resolution system, so that our risk ecosystem mitigates financial losses while empowering growth, our risk infrastructure is robust yet provides the flexibility required to support various businesses, our resolution to potential issues and risks are prompt, intelligent and accurate, and eventually, our community transacts with confidence and ease on Airbnb.

What is Machine Learning Engineer on Payments Risk at Airbnb? 

We are looking for talented and self-motivated machine learning engineers who are passionate about working in this highly dynamic domain and collaborating with a world-class XFN team to advance our mission. As part of the Payments Risk Modeling  team, you will work across all parts of Airbnb’s ML stack from writing data pipelines to analyzing data, training models, running controlled experiments and building model serving related infrastructure and services. You will be deeply involved in the technical details of building highly available and real-time ML models in close collaboration with product, backend/full stack engineers, data science and operations teams to understand and react to the ever evolving risk scenarios and to make Airbnb the world’s safest while easiest to use payments platform. 

 

Responsibilities

  • Work with large scale user behavioral data to build ML products to detect fraud activities and ensure financial compliance.
  • Collaborate closely with PM and Data Scientists to identify opportunities for business impact, leverage data to quantify outcome
  • Work closely with product and infra engineers to understand, refine, and prioritize requirements for shared machine learning models
  • Leverage third-party ML tools and in-house models & infrastructure, or build new ML systematic solutions tailored to Airbnb Payments problems
  • Hands on development, productionize and operate ML models and pipelines at scale, including both batch and real-time use cases
  • Improve the quality of existing ML models and infrastructure.

 

Minimum Qualifications/Requirements

  • 5+ years of industry experience or a PhD + 2 years industry experience in applied machine learning.
  • Experience developing machine learning models at scale from inception to impact
  • Strong coding skills in Python/Java/Scala or equivalent
  • Experience with distributed data processing tools like Hive, Spark, Airflow and popular ML frameworks like Tensorflow or Pytorch is preferred
  • Solid understanding of engineering best practices and complexities of models in production
  • Strong understanding of neural networks/deep learning, feature engineering, feature selections and optimization algorithms.

Locations

  • We’re hiring in both our San Francisco and Seattle offices.