Senior Staff Machine Learning Engineer, Trust

$244,000 - $244,000/Yr

Airbnb - San Francisco, CA

Full-time - Senior

Hybrid - San Francisco, CA

1,001-5,000 employees

Accommodation

About the position

Airbnb was born in 2007 when two Hosts welcomed three guests to their San Francisco home, and has since grown to over 4 million Hosts who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Every day, Hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Trust team at Airbnb is dedicated to ensuring safety and security within the community. This team is responsible for protecting the platform from various forms of fraud, both online and offline, while also ensuring that hosts, guests, homes, and experiences meet high standards. The Trust Engineering team is tasked with developing the technology vision and infrastructure that supports these efforts, focusing on complex topics such as identity and reputation to foster trust in every interaction with Airbnb. As a Senior Staff Machine Learning Engineer, you will play a crucial role in this mission. You will work closely with senior leaders across the technical organization, contributing as a hands-on software engineer. Your responsibilities will include working with large-scale structured and unstructured data, building and continuously improving cutting-edge machine learning models for various Airbnb use cases. You will collaborate with cross-functional partners, including software engineers, product managers, and data scientists, to identify opportunities for business impact and drive engineering decisions. Your work will involve developing, productionizing, and operating machine learning models and pipelines at scale, addressing both batch and real-time use cases. Examples of your contributions may include anomaly detection models and continuous risk evaluation models.

Responsibilities

• Work with large scale structured and unstructured data to build and improve machine learning models.
• Collaborate with cross-functional partners to identify business impact opportunities and refine requirements for machine learning models.
• Drive engineering decisions and quantify impact of machine learning initiatives.
• Develop, productionize, and operate machine learning models and pipelines at scale, including batch and real-time use cases.
• Work closely with trust defense and platform teams to address evolving fraud attack landscapes.

Requirements

• 12+ years of industry experience in applied Machine Learning.
• A Bachelor's, Master's or PhD in Computer Science, Machine Learning, or a related field.
• Strong programming skills in Scala, Python, Java, C++, or equivalent.
• Deep understanding of machine learning best practices, algorithms, and domains such as natural language processing and anomaly detection.
• Experience with technologies such as Tensorflow, PyTorch, Kubernetes, Spark, Airflow, Kafka, and data warehouses like Hive.
• Industry experience in building end-to-end machine learning infrastructure and productionizing models.
• Exposure to architectural patterns of large, high-scale software applications.

Nice-to-haves

• Experience with test-driven development and A/B testing.
• Familiarity with the Trust and Risk domain.

Benefits

• Base pay range of $244,000—$304,000 USD, subject to change.
• Eligibility for bonus, equity, benefits, and Employee Travel Credits.