Staff Machine Learning Engineer

Summary

Join Airbnb's Core ML team and leverage your expertise in machine learning and AI to develop cutting-edge AI-powered products. As a machine learning engineer or scientist, you will work with large-scale data, build and improve ML models and pipelines, and collaborate with cross-functional partners to identify opportunities for business impact. You will be involved in the entire AI product development lifecycle, from inception to production at scale, using agile practices. This US-based remote-eligible position requires a PhD in a related technical field and 7+ years of industrial experience, including experience with Generative AI. The role offers a competitive salary, bonus, equity, benefits, and Employee Travel Credits.

Requirements

• PhD in Computer Science, Mathematics, Statistics, or related technical field
• 7+ years of industrial experience in building, testing and shipping AI models and products from inception to production; including 1+ years of experience with GenAI
• Deep knowledge and hands-on experience with Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. NLP, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection)
• Experience with AI technologies in customer support or chatbot related applications
• Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain
• Ability to absorb new concepts quickly and integrate them effectively into business processes

Responsibilities

• 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 Machine Learning 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

Benefits

This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits