About the Role
As a Senior ML Engineer for Payments, you will be the catalyst that transforms bold AI innovation - LLM-powered 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 solutions at global scale, partner closely with product, software, and operations teams to turn complex requirements into elegant, latency-first services, and set the technical standard for model governance, continuous learning, and engineering excellence that elevates our entire payments ecosystem while shaping the company’s broader AI strategy.
Responsibilities
- 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.
Requirements
- 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.
- Hands-on experience with at least three of the following: PyTorch/TensorFlow, scalable inference stacks, vector search, orchestration/MLOps platforms (Kubeflow, Airflow), large-scale 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.