Role Overview
The Growth Platform team at Airbnb is dedicated to driving long-term sustainable growth for the Airbnb community. Our mission is to build a best-in-class agentic system and capabilities to support the growth of all Airbnb products, current and future. We achieve this by delivering highly personalized and relevant content and product experiences to the Airbnb community, both on and off of the Airbnb platform. The north star is full autonomy — where AI identifies opportunities, creates campaigns, personalizes experiences, and optimizes outcomes with minimal human intervention. We are progressing along a deliberate maturity curve: AI assisted → agentic → autonomous, with human-in-the-loop controls at every stage to ensure brand safety, quality, and compliance.
The Growth Platform deeply integrates with the Airbnb product, understanding the customer journey and enabling differentiated customer engagement experiences to drive product growth. The platform also powers digital marketing channels including landing pages, email, push, SMS, and digital advertising, as well as the machine learning/AI and data platforms that feed into the management and optimization of these channels.
The Difference You Will Make
- As a machine learning engineer or scientist, your expertise will be pivotal in developing AI-powered solutions to shape the future of the Airbnb agentic growth platform with cutting-edge AI techniques. You will drive and guide the rest of the engineers to brainstorm, design and develop AI products and features from inception to production.
- We're seeking a Senior Staff Engineer who thrives at the intersection of technical depth, architectural thinking, and mentorship.
- You’ll collaborate with cross-functional leaders, build resilient systems that operate globally at scale, and help evolve the foundational building blocks behind AI-powered growth systems.
Some example projects you will work on:
- AI-Powered Content Generation: Developing agentic capabilities to autonomously create personalized emails, push notifications, Ad copy, and creatives. This significantly scales marketing efforts by enabling more campaigns, greater variant testing, and faster iteration cycles.
- ML/AI Orchestration for Decisioning: Utilizing AI to determine the optimal audience, message, channel, and timing for communications. This shifts marketing decision-making to model-driven intelligence, enhancing relevance and minimizing message fatigue. The direct impact is an uplift in engagement rates, conversion, and ultimately, bookings.
- Proactive Marketing Analyst Agent: Designing an AI agent that autonomously identifies new marketing opportunities and converts them into executable campaigns. It leverages world knowledge, proprietary Airbnb intelligence, and deep customer profiles. A crucial performance-based feedback loop ensures the system continuously learns from campaign outcomes to refine and improve future recommendations.
A Typical Day
- Work with large scale structured and unstructured data; explore, experiment, build and continuously improve Machine Learning models and pipelines for Airbnb product, business and operational use cases.
- Work collaboratively with cross-functional partners including product managers, operations and data scientists, to identify opportunities for business impact; understand, refine, and prioritize requirements for machine learning, and drive engineering decisions.
- Hands-on develop, productionize, and operate ML/AI models and pipelines at scale, including both batch and real-time use cases.
- Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.
- Collaborate actively with engineers to apply ML / AI in their solutions to help validate ideas and guide to the right outcomes.
- Partner with ML/AI Engineers in foundations engineering to mentor and develop initiatives that make ML/AI applications a core discipline for non-ML/AI engineers.
Your Expertise
- 12+ years of industry experience in applied ML/AI, inclusive MS or PhD in relevant fields.
- Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.
- Deep understanding of ML/AI best practices (e.g. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (e.g. neural networks/deep learning, optimization) and domains (e.g. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection).
- Deep understanding of Machine Learning best practices (e.g. training/serving skew minimization, A/B test, feature engineering, feature/model selection) and algorithms (e.g. gradient boosted trees, neural networks/deep learning, optimization).
- Experience with technologies such as: Tensorflow, PyTorch, Kubernetes, Airflow (or equivalent), Kafka (or equivalent).
- Expertise with architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).
Preferred Qualifications
- Agentic and Automation: Experience with AI technologies in automating processes and developing agentic solutions and frameworks.
- Agile Practice for AI Production: Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain.
- Infrastructure Acumen: Experience building robust testing frameworks for agent behavior validation and continuous improvement, and driving architectural requirements on ML infrastructures.