Senior Machine Learning Engineer (Trust)
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Sep 30, 2026. Airbnb scores B on the Alion truth index.
Join Airbnb's Trust team as a Senior Machine Learning Engineer, where you'll develop technology to protect the community from fraud and ensure high standards for hosts, guests, homes, and experiences. You'll work closely with product managers, data scientists, software engineers, and operations teams to design and build ML solutions that have a direct impact on user trust and business success. Your work will help reduce risks posed by bad actors while ensuring the platform remains seamless and welcoming for everyone else.
Missions
- Contribuer activement au code et aux idées qui façonnent les systèmes d'apprentissage automatique protégeant des millions d'utilisateurs d'Airbnb.
- Posséder et livrer des projets d'apprentissage automatique de bout en bout, de la conception et de l'entraînement des modèles à leur production et à leur exploitation à grande échelle.
- Collaborer avec des équipes interfonctionnelles pour identifier des opportunités, définir des solutions d'apprentissage automatique et affiner les exigences pour de nouveaux modèles de confiance.
Profil recherché
- Strong programming skills in Python (required) and familiarity with Scala, Java, or equivalent- Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high-volume data pipelines, efficient algorithms)
- A Bachelor's, Master's, or PhD in CS/ML or a related field
- Experience with data engineering and building end-to-end ML pipelines, including both batch and real-time systems
- Exposure to the Trust and Risk domain (e.g., fraud detection, anomaly detection, identity, account integrity) is a plus
- Experience with test-driven development, incremental delivery, and deployment practices
- Experience with ML frameworks and tooling such as TensorFlow, PyTorch, or equivalent
- 5+ years of industry experience in applied Machine Learning, with a track record of building and productionizing models at scale
- Solid understanding of Machine Learning best practices - e.g., training/serving skew minimization, A/B testing, feature engineering, model selection - and algorithms such as gradient boosted trees, neural networks, transformers, and deep learning