Senior Staff Machine Learning Engineer, Growth Platform Engineering

Details

• 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.
• 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.
• 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.
• 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 (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection) and algorithms (eg. 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)
• 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