Staff Software Engineer – ML Infrastructure 136 views

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 Community You Will Join

At Airbnb, their mission is to create a world where anyone can belong anywhere. They use Machine Learning extensively to create a more connected, empowered, and safer global community and enable an intelligent & worry-free travel experience.

The Difference You Will Make

You’ll help us build Airbnb’s end-to-end ML Platform – a scalable shared platform that accelerates the pace of Machine Learning design & development and the deployment of impactful, high-quality ML use cases company-wide.

A Typical Day

You’ll have the opportunity to work on a wide variety of projects that support the ML lifecycle from ideation to production, including:

• Build scalable distributed training systems to power groundbreaking models, leveraging the latest algorithms, techniques and hardware available
• Create robust and scalable real-time ML inference systems meeting demanding requirements for low-latency, efficiency, and workflow flexibility
• Build ergonomic ML Workflows & Experimentation tracking tools to accelerate development / iteration productivity for ~200 ML Engineers & Data Scientists.
• Ensure real-time detection of problems afflicting ML models via ML Observability tools
• Build on top of OSS ML Platform technologies such as Kubeflow, ML Flow, and Tensorflow Serving
• Work side by side with customer teams including Search Relevance, Trust & Safety, Customer Support to create a better product experience for Airbnb users.

Your Expertise

• 8+ years of industry experience (and/or relevant academic experience)
• Strong coding skills in Python/Java or equivalent
• Solid understanding of engineering and infrastructure best practices
• Experience developing and productionizing machine learning models is a plus
• Experience with Docker, Kubernetes, Spark is a plus
• Industry experience building end-to-end Machine Learning Platforms a big plus