Senior Machine Learning Engineer, Payments

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 conne...

Payments is key for any healthy marketplace, and is just as central to our product at Airbnb. The Payments org at Airbnb is responsible for everything related to settling money in Airbnb’s global marketplace and makes the Payment experience as delightful, magical, intuitive, and easy as possible. At the Payments mod...

As a Senior ML Engineer for Payments, you will be the catalyst that transforms bold AI innovation - LLMpowered workflow, realtime fraud defenses, and hyperpersonalized checkout flows - into production systems that make Airbnb Payment experience feel effortless and secure; you’ll architect and own end-to-end solution...

Spearhead LLM agents, realtime anomaly detectors, and other breakthrough solutions that solve real-world problems and create product magic.
Collaborate with product, engineering, ops, and data science to spot high leverage opportunities, refine AI/ML requirements, make principled architecture choices, and measure business value with clear, data-driven metrics.
Design, train, deploy, and operate large-scale AI applications for both batch and streaming workloads, ensuring low latency, high reliability, and continuous improvement via automated monitoring and retraining loops.
Mentor and inspire teammates, fostering a collaborative, experimentation-driven environment where cutting edge research meets production excellence and every engineer is empowered to push AI boundaries at Airbnb.

5+ years of industry experience in applied AI/ML, inclusive MS or PhD in relevant fields.
Strong programming (Python/Java) and data engineering skills.
Proven mastery of modern AI/LLM workflows — prompt engineering, fine tuning (LoRA, RLHF), hallucination mitigation, safety guardrails, and rigorous online/offline testing to minimize training/inference drift and ensure reliable outcomes.
Handson experience with at least three of the following: PyTorch/TensorFlow , scalable inference stacks, vector search, orchestration/MLOps platforms (Kubeflow, Airflow), largescale data streaming & processing (Spark, Ray, Kafka)
Demonstrated success designing, deploying, and monitoring production AI systems - e.g. personalization engines, generative content services - complete with drift/cost/latency monitoring, automated retraining triggers, and cross-functional collaboration that translates ambiguous business needs into measurable AI impact.
Prior knowledge of AI/ML Applications in the Payments domain is highly desirable