Staff Software Engineer - ML Ops Platform

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

In this role, you’ll help us build Airbnb’s end-to-end MLOps 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.

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 ~150 ML Engineers & Data Scientists.
• Ensure real-time detection of problems afflicting ML models via ML Observability tools
• Create ML Governance tooling to ensure that all ML use cases adhere to high quality standards and policies
• Utilize OSS ML Platform technologies such as Kubeflow, ML Flow, or Metaflow
• Work side by side with customer teams including Search Relevance, Trust & Safety, Customer Support to deliver high-impact product wins

Who we are looking for:

• 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

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status