Staff Machine Learning Engineer - Marketing Technology
- San Francisco, CA
Airbnb is one of the world’s largest marketplaces for unique, authentic places to stay and things to do, offering over 7 million accommodations and 40,000 handcrafted activities, all powered by local hosts. As an economic empowerment engine, Airbnb has helped millions of hospitality entrepreneurs monetize their spaces and their passions while keeping the financial benefits of tourism in their own communities. With more than half a billion guest arrivals to date, and accessible in 62 languages across 220 countries and regions, Airbnb promotes people-to-people connection, community and trust around the world.
About the Marketing Technology Team
Marketing Technology is a platform team with the goal of delivering best-in-class marketing automation tools and systems that are used by our stakeholders in Marketing (Brand and Performance), the Guest Experience and Hosting product development teams, Policy, and more. Our Mission is to enable marketing and product teams to deliver highly personalized and relevant content to the Airbnb community, both on-site and off-site.
The Marketing Technology team spans five pillars: (1) Demand side platform (DSP) that powers programmatic advertising - both search and display, (2) Email marketing platform, (3) Landing page platform, (4) Communications and delivery infrastructure that enables promotional and transactional content delivery at scale across multiple offline channels (e.g. email, push, sms), (5) Machine learning and data pipelines that enable optimizations and personalization.
About The Role
We are looking for a ML Engineer to join the Marketing Technology ML team. In this role, you’ll have the opportunity to build a portfolio of reusable and scalable ML models that enable Airbnb to reach the right customer at the right time using the right channel with the right content.
This will be done through collaborating with various teams in Airbnb including search infra, search relevance, teams within Marketing Technology to build and improve personalization for content, send time, targeting and a solid ML foundation.
- Work with large scale user behavioral data to build ML products, examples include but not limited to :
- Targeting models that help identify key user segments that can be used to optimize engagement with a user (ex. booking intent, user engagement rate predictions)
- Content optimization models that identify right content to showcase to the user (ex. Personalize and Rank content sections in an email/ landing page)
- Support omni-channel engagement by identifying the right channel and right time to engage with users.
- Collaborate closely with PM and Data Scientists to identify opportunities for business impact, leverage data to quantify outcome and participate in planning ML strategy and roadmap for the team.
- 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 problems
- Hands on development, productionize and operate ML models and pipelines at scale, including both batch and real-time use cases
- Improve quality of existing ML models and infrastructure.
- Develop scalable, reliable distributed systems.
- Lead projects and mentor junior engineers
- 8+ years of industry experience or a PhD + 6 years relevant industry experience
- Experience developing machine learning models at scale from inception to impact
- Strong coding skills in Python/Java/Scala or equivalent
- Experience with C++, Spark a plus
- Solid understanding of engineering best practices and complexities of models in production
- Exposure to architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
- Experience with test driven development, familiar with A/B testing, incremental delivery and deployment.
- Bonus: Experience building platforms, specifically marketing tools/technology, campaign management, ad tech
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