Senior Machine Learning Engineer, Payments

Airbnb · Remote-USARemotosenior

En corto

▸Ingeniero ML senior que transforma IA de vanguardia (LLM, detección en tiempo real) en sistemas de pagos a escala global.
▸Día a día: diseñar, desplegar y monitorear modelos de IA en producción, colaborando con múltiples equipos para optimizar pagos y prevención de fraude.
▸Destacado: liderar la integración de IA generativa (LLM) en flujos críticos de pagos con enfoque en latencia, seguridad y gobernanza.

Experiencia con sistemas de IA en producción con enfoque en latencia, escalabilidad y gobe

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¿Qué piden?

✓5+ años de experiencia en IA/ML aplicada
✓Maestría o doctorado en campo relevante
✓Dominio de Python/Java y engineering de datos
✓Experiencia comprobada con LLMs: fine tuning, prompt engineering, mitigación de hallucinaciones
✓Habilidades en MLOps: orquestación (Airflow, Kubeflow), streaming (Kafka, Spark)
✓Capacidad para diseñar y operar sistemas de IA en producción con monitoreo automático

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PythonJavaPyTorchTensorFlowLLMsLoRARLHFKubeflowAirflowSpark

¿A quién escribirle en Airbnb?

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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 Community You Will Join: 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 modeling team, our goal is to empower the mission by introducing intelligences and personalizations that optimize Airbnb’s massive daily transaction volume to best collect payments from guests, distribute payouts to hosts while preventing fraudulent transactions from happening. The Difference You Will Make: 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 solutions at global scale, partner closely with product, software, and operations teams to turn complex requirements into elegant, latencyfirst 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. A Typical Day: • 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. Your Expertise: • 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

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