Tech Stack

Description

You will be the catalyst that transforms bold AI innovation — LLM-powered workflows, real-time fraud defenses, and hyper-personalized checkout flows — into production systems that make Airbnb's Payment experience feel effortless and secure. You’ll architect and own end-to-end solutions at global scale, partnering with product, software, and operations teams to turn complex requirements into elegant, latency-first services. You will set the technical standard for model governance, continuous learning, and engineering excellence that elevates our entire payments ecosystem.

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.

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.