ML Engineer

📋 Description

• Standardize model quality across all integrity signals.
• Build evaluation infra, golden datasets, and benchmarks.
• Own performance improvement strategy for each signal.
• Define ML strategy for new signals: audio, gaze, behavior.
• Monitor fraud tooling and adapt approaches as needed.
• Identify edge cases and train data to cover them.

🎯 Requirements

• Shipped ML systems in production that users depend on.
• Strong intuition for where precision leaks happen.
• Think in systems: signal accuracy, data pipelines, serving.
• Value proper evaluation methodology; wrong metrics hurt.
• Curious about adversarial dynamics and fraud detection.
• Nice to have: multimodal systems in production.