Principal Machine Learning Engineer- LLM Fine-tuning and Optimization

Airbnb Airbnb · Consumer · United States · Software Engineering

Principal Machine Learning Engineer focused on fine-tuning and optimizing LLMs for Airbnb's consumer-facing products, including AI Assistants and autonomous agents. The role involves working with large datasets, developing multi-year tech roadmaps, evaluating foundational models, and productionizing LLM pipelines. Requires extensive experience in model training, fine-tuning, optimization, and post-training techniques, with a strong preference for PhDs and publication records in AI conferences.

What you'd actually do

  1. Work with large scale structured and unstructured data; explore, experiment, build and continuously improve foundation models for Airbnb product, business and operational use cases.
  2. 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.
  3. Continuously evaluate recent and upcoming large foundational models, ensuring the selection and refinement of the highest quality models for enhanced performance and efficiency.
  4. Hands-on prototype, develop and productionize LLM models and pipelines at scale, including both batch and real-time use cases.
  5. Drive key AI architectural decisions for products, and contribute to Airbnb’s ML platform architecture and strategy.

Skills

Required

  • 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
  • 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 have

  • 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

What the JD emphasized

  • 10+ years of experience with developing machine learning models and products at scale from inception to business impact.
  • Proven record of training, fine tuning, optimizing models and inference run-time
  • Post-training experience
  • Or specialized experience

Other signals

  • LLM fine-tuning
  • model optimization
  • high-performance deployment
  • AI Assistant
  • Autonomous agent
  • recommendation
  • travel planning
  • foundation models
  • large foundational models
  • LLM models and pipelines
  • batch and real-time use cases
  • AI architectural decisions
  • ML platform architecture
  • training
  • fine tuning
  • optimizing models
  • inference run-time
  • Post-training experience
  • responsible LLMs
  • LLM alignment
  • reinforcement learning
  • efficient training and inference
  • language model evaluation
  • multilingual and multimodal modeling
  • runtime optimizations
  • model quantization
  • compression
  • on-device inference
  • GPU inference
  • pytorch
  • kernel development
  • AI technologies in customer support applications
  • AI product development lifecycle
  • Applied AI/ML domain
  • deploying and scaling business-critical AI services
  • architectural requirements on ML infrastructures