Machine Learning Engineer, Integrity

Hybrid In Santa Clara, United StatesHybrid

Responsibilities

Build the evaluation infrastructure, golden datasets, and benchmarking pipelines that give us and our customers genuine confidence in what we ship
Own the performance improvement strategy for each signal.
Define the ML strategy for new signals from scratch: audio analysis, gaze tracking, behavioral anomalies.
Continuously monitor how assessment fraud tooling is evolving. Evaluate new models as they emerge. Know when to abandon a strategy that is no longer moving the needle
Drive strategy-level decisions: which new signals to build, whether to use models at all, and what the evidence says Who you are

Software has entered an era where humans and AI build side by side.
Integrity isn't about whether you use AI or not; it's about whether you are following the rules.
What is needed is calibrated uncertainty at the signal level, a principled way to weight evidence depending on conditions, and the ability to detect when a signal has drifted out of its reliable operating range.
You have shipped ML systems in production that real users and real businesses depend on
Experience with multimodal systems in production: vision, audio, or behavioral signal pipelines
Background in adversarial ML or fraud/anomaly detection
experience defining what production-ready means for a new signal category from scratch You will thrive here if

Our integrity system is a portfolio of signals spanning vision, code analysis, browser telemetry, and behavioral sequences.
You define what the problem even is, and you hold yourself to a higher bar than the one the market has set. Compensation
The annual US on-target earnings (OTE) range for this role is $120,000 – $235,000 which includes base salary and target bonus.
Compensation for this role includes base salary, target bonus, and equity.