ML Engineer

📋 Description

• Standardize model quality: define, measure, and report across signals.
• Own performance improvements for each signal; explore new architectures.
• Define ML strategy for new signals (audio, gaze, behavior) with clear production bars.
• Continuously monitor fraud tooling; evaluate new models; discard stagnant approaches.
• Surface edge cases; collect data around them; harden models.
• Drive strategy decisions: signal choices and model usage backed by evidence.

🎯 Requirements

• Shipped ML systems in production used by real users and businesses.
• Deep intuition for precision leaks and how to locate them.
• Think in systems: signal accuracy, data pipeline, serving infra, customer outcome.
• Care about evaluation methodology; a wrong metric is worse than none.
• Curious about adversarial dynamics; attacks are interesting, not exhausting.
• Experience with multimodal systems in production: vision, audio, or behavioral signals.

🎁 Benefits

• Equal employment opportunity and affirmative action employer.
• We are an equal opportunity employer; inclusive to all.