Senior Staff Machine Learning Engineer, Post Training

About the role

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The CS AI product team is responsible for driving CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting the Generative AI technologies to enable an intelligent, scalable and exceptional service experience. The team develops and enhances various AI models, ML services and tools including LLM fine-tuning, alignment and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. As a senior staff 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.

Key facts

What you'll do

Analyze large-scale structured and unstructured data to experiment, build, and continuously refine foundational models tailored for Airbnb product, business, and operational requirements. Architect a multi-year technology roadmap that guides our team along the rapidly evolving AI landscape while leveraging best-in-class innovations to deliver measurable customer benefits. Conduct rigorous evaluation of emerging and upcoming large foundational models to ensure the selection and meticulous refinement of the highest quality models for enhanced performance and efficiency. Prototype, develop, and productionize large language model pipelines at scale, handling both batch and real-time use cases with robustness and reliability. Champion and drive key AI architectural decisions for products while actively contributing to Airbnb's ML platform architecture and long-term strategy. Implement advanced post-training techniques, including data processing for fine-tuning, responsible LLM practices, alignment methods, reinforcement learning, efficient training and inference, language model evaluation, and multilingual and multimodal modeling. Optimize runtime performance, model quantization, compression, on-device inference, and GPU inference to meet stringent latency and resource constraints across Airbnb's infrastructure. Collaborate closely with product managers, software engineers, data scientists, and operations teams to brainstorm, design, and develop AI-powered products such as AI Assistant, Autonomous agent, recommendation, and travel planning tools that create meaningful impact. Establish and maintain rigorous evaluation frameworks and testing automation to continuously monitor model quality, safety, and alignment in production environments. Mentor and influence engineering best practices across the organization by documenting patterns, leading code reviews, and guiding less experienced team members on complex ML challenges.

Requirements

Hold a PhD in Computer Science, Machine Learning, Mathematics, Statistics, or a related technical field demonstrating deep theoretical and practical expertise. Bring 10+ years of experience developing machine learning models and products at scale from initial conception through measurable business impact. Possess strong programming proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch. Demonstrate a proven record of training, fine-tuning, and optimizing models alongside managing inference run-time performance in production settings. Show substantial post-training experience in areas such as data processing for fine-tuning, responsible LLM development, LLM alignment, reinforcement learning, efficient training and inference strategies, language model evaluation, and multilingual or multimodal modeling. Exhibit specialized experience in runtime optimizations, model quantization, compression techniques, on-device inference, GPU inference, PyTorch internals, and kernel development to meet extreme performance targets. Maintain a strong commitment to ethical AI practices, ensuring guardrails, safety evaluations, and testing automation are integral to deployed AI products. Communicate effectively and collaborate seamlessly with diverse stakeholders across global teams, translating complex technical concepts into actionable product decisions. Thrive in an environment that demands ownership, deep exploration, and rapid iteration on ambiguous problems that directly influence the traveler and host experience.

Nice to have

Demonstrated publications or contributions at top-tier conferences and journals in machine learning or related fields. Experience with open-source ML communities or contributions to widely adopted libraries. Background in building and scaling consumer-facing AI products in high-traffic internet services. Familiarity with distributed systems and infrastructure orchestration platforms used in large-scale ML deployments.

Practical notes

This is a full-time position based in the United States. Compensation details are provided in ranges to reflect regional differences and may vary based on factors such as the candidate's skills and experience, cost of living, and relevant work experience. Relocation assistance is not provided for this role. The engagement is full-time, and compensation includes base salary, target bonus, and equity. All applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, or age. Nurseries are not provided at this location.