About the job
As a principle machine learning engineer, you will be responsible for fine-tuning state-of-the-art LLMs for diverse use cases while optimizing models for high-performance deployment on Airbnb’s ML Infrastructure. You will partner with product managers, software engineers, data scientists and operation teams to brainstorm, design and develop AI products such as AI Assistant, Autonomous agent, recommendation, travel planning, and many more products that make meaningful impacts in the world of travel.
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.
Qualifications
Minimum
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
Preferred
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