Principal Machine Learning Engineer- LLM Fine-tuning and Optimization
Principal MLE focused on fine-tuning and optimizing large language models for production, building scalable AI pipelines, and shaping Airbnb's ML platform strategy. Requires 10+ years experience and a PhD.
About the job
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.
Skills
Python, PyTorch, Llm Fine-Tuning, Model Optimization, Model Quantization, Reinforcement Learning, Gpu Inference, Model Compression, Kernel Development, On-Device Inference
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