Machine Learning Engineer, Integrity
HackerRank
full-time
Posted on:
Location Type: Hybrid
Location: Santa Clara • California • United States
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About the role
- Standardize how model quality is defined, measured, and reported across all integrity signals. 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. Explore newer architectures, emerging research, and different training paradigms. The approach will not be one-size-fits-all; it will be grounded in each signal's maturity, data quality, and what the science actually supports
- Define the ML strategy for new signals from scratch: audio analysis, gaze tracking, behavioral anomalies. Set the architecture, data requirements, and a clear bar for what production-ready looks like before anything ships
- 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
- Systematically surface edge cases, build training data around them, and turn every customer-reported failure into a model that is harder to fool
- Drive strategy-level decisions: which new signals to build, whether to use models at all, and what the evidence says
Requirements
- You have shipped ML systems in production that real users and real businesses depend on
- You have deep intuition for where precision leaks happen and how to find them systematically, not by luck
- You think in systems. A signal's accuracy number, its data pipeline, its serving infrastructure, and its customer-facing outcome are one problem to you
- You care as much about evaluation methodology as model performance. You know that a metric measured wrong is worse than no metric
- You are genuinely curious about adversarial dynamics. The fact that your model will be attacked is interesting to you, not exhausting.
- Experience with multimodal systems in production: vision, audio, or behavioral signal pipelines
- Background in adversarial ML or fraud/anomaly detection
- Publications or open-source work in detection, robustness, or model evaluation
- Prior experience defining what production-ready means for a new signal category from scratch
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningmodel evaluationdata pipelineadversarial machine learninganomaly detectionmultimodal systemsaudio analysisgaze trackingbehavioral signalsperformance improvement
Soft Skills
system thinkingcuriosityattention to detailstrategic decision makingproblem solving