Senior Machine Learning Engineer, Trust

What you'll do

• Design, build, and productionize end-to-end machine learning pipelines for both batch and real-time use cases.
• Develop ML solutions to detect fraud, including account takeovers, fake accounts, payment fraud, and bot activity.
• Write, review, and ship clean, testable code for model training, pipeline improvements, and feature optimization.
• Analyze large-scale structured and unstructured data to improve models for product and operational use cases.
• Investigate emerging fraud patterns and threat signals to develop proactive ML-based detection tools.
• Manage the full ML lifecycle including feature engineering, model training, evaluation, and deployment at scale.
• Collaborate with cross-functional teams to scope requirements and refine trust models based on evolving fraud landscapes.

What we're looking for

• 5+ years of industry experience in applied Machine Learning, including building and productionizing models at scale.
• Strong programming skills in Python are required.
• Familiarity with Scala, Java, or equivalent languages.
• Solid understanding of ML best practices like A/B testing, feature engineering, and algorithms such as neural networks and transformers.
• Experience with ML frameworks and tooling including TensorFlow, PyTorch, or equivalent.
• Experience with data engineering and building end-to-end ML pipelines for batch and real-time systems.
• Exposure to architectural patterns of large, high-scale software applications.
• A Bachelor's, Master's, or PhD in CS/ML or a related field.
• Exposure to the Trust and Risk domain (e.g., fraud detection, anomaly detection) is preferred.