Tech Stack

Description

You will define and drive the technical strategy for integrating ML capabilities into Airbnb's messaging products, including smart replies, message classification, content moderation, translation, and conversational assistance. You'll own the full lifecycle of ML-powered features from prototyping to production, partnering with ML and product teams.

Requirements

• 9+ years of relevant engineering hands-on work experience
• Bachelors, Masters, or PhD in CS or related field
• Demonstrated experience building and shipping ML-powered product features in production environments, including model serving, feature pipelines, online/offline evaluation, and monitoring
• Exceptional architecture abilities and experience with architectural patterns of large, high-scale applications
• Familiarity with NLP/NLU techniques and large language models, particularly as applied to messaging, conversational AI, or content understanding
• Shipped several large-scale projects with multiple dependencies across teams, specifically at the intersection of ML infrastructure and product engineering
• Technical leadership and strong communication skills with ability to translate between ML research, product goals, and engineering execution
• Experience operating distributed, real-time systems at scale with high reliability requirements
• Experience with real-time messaging systems or event-driven architectures
• Familiarity with ML infrastructure at scale (e.g., feature stores, model registries, online inference platforms)
• Prior work on trust safety, content moderation, or internationalization in a messaging context
• Experience with LLM-based product features, including prompt engineering, retrieval-augmented generation, or fine-tuning

Responsibilities

• Design, build, and operate systems that serve ML models within the messaging stack, focusing on latency, reliability, and scalability
• Write and review technical designs for large, open-ended problems at the intersection of ML and product engineering
• Partner with ML, data science, and product teams to identify high-value opportunities and close the gap between offline model performance and production impact
• Collaborate with engineers and cross-functional partners across Messaging, Trust Safety, Localization, and Platform organizations
• Mentor, guide, advocate, and support the career growth of individual contributors
• Establish engineering standards for ML integration across the messaging surface, including feature flagging, A/B testing, observability, and graceful degradation