Principal Machine Learning Engineer- LLM Fine-tuning and Optimization

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.

Nice-to-Haves

• 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.