Principal Machine Learning Engineer- LLM Fine-tuning and Optimization

Airbnb, founded in 2007, has grown into a global leader in travel and hospitality, connecting millions of hosts and guests. They are seeking a Principal Machine Learning Engineer to lead the fine-tuning and optimization of large language models (LLMs) for various applications, collaborating with cross-functional teams to develop impactful AI products.

Responsibilities:

• Work with large scale structured and unstructured data; explore, experiment, build and continuously improve foundation models for Airbnb product, business and operational use cases
• Create a multi-year tech roadmap that enables our team to stay on the leading edge of the rapidly evolving AI landscape and leverage the best in class technologies to deliver customer benefits
• Continuously evaluate recent and upcoming large foundational models, ensuring the selection and refinement of the highest quality models for enhanced performance and efficiency
• Hands-on prototype, develop and productionize LLM models and pipelines at scale, including both batch and real-time use cases
• Drive key AI architectural decisions for products, and contribute to Airbnb’s ML platform architecture and strategy

Requirements:

• PhD in Computer Science, Machine Learning, Mathematics, Statistics, or related technical field
• 10+ years of experience with developing machine learning models and products at scale from inception to business impact
• Programming experience in Python and hands-on experience with frameworks such as PyTorch
• Proven record of training, fine tuning, optimizing models and inference run-time
• Post-training experience in areas like data processing for fine-tuning; responsible LLMs; LLM alignment; reinforcement learning; efficient training and inference; language model evaluation; and/or multilingual and multimodal modeling
• Or specialized experience in runtime optimizations, model quantization, compression, on-device inference, GPU inference, pytorch, kernel development
• PhD in AI, machine learning, data science, or related technical fields
• Publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL)
• Customer Support Systems: Experience with AI technologies in customer support applications
• 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 deploying and scaling business-critical AI services and driving architectural requirements on ML infrastructures