Senior Machine Learning Engineer, Trust

About This RoleAI processing…

Key Responsibilities

• 1
  Collaborate with product managers, data scientists, software engineers, and operations teams to identify opportunities, scope ML solutions, and refine requirements for new or improved Trust models.
• 2
  Design, build, and productionize end-to-end Machine Learning pipelines — including feature engineering, model training, evaluation, and deployment — for both batch and real-time use cases.
• 3
  Investigate emerging fraud patterns and threat signals with your teammates, and develop ML-based detections and tools that enable faster, more accurate responses.
• 4
  Write, review, and ship clean, testable code — whether training a new model, improving an existing pipeline, or optimizing a feature for scalability and reliability.
• 5
  Work with large-scale structured and unstructured data to continuously improve ML models for Airbnb product, business, and operational use cases.
• 6
  Participate in code reviews, design discussions, and cross-team collaborations to contribute to a high-quality ML engineering culture.
• 7
  Work closely with trust defense and platform teams to adapt models and systems to an evolving landscape of fraud attacks.

Requirements

• 5–10 years of industry experience in applied Machine Learning, with a track record of building and productionizing models at scale.
• Strong programming skills in Python (required) and familiarity with Scala, Java, or equivalent.
• 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.
• Experience with ML frameworks and tooling such as TensorFlow, PyTorch, or equivalent.
• Experience with data engineering and building end-to-end ML pipelines, including both batch and real-time systems.
• Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high-volume data pipelines, efficient algorithms).
• Experience with test-driven development, incremental delivery, and deployment practices.
• Exposure to the Trust and Risk domain (e.g., fraud detection, anomaly detection, identity, account integrity) is a plus.
• A Bachelor's, Master's, or PhD in CS/ML or a related field.

Perks & Benefits