Machine Learning Engineer, Integrity
HackerRank - Hybrid in Santa Clara, CA
Posted Apr 9, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Machine Learning Engineer, Integrity Hybrid in Santa Clara, CA HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world's most innovative companies to build strong engineering teams ready for what's next. Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills. People at HackerRank care deeply about the impact of their work and sweat the small details so our customers can be wildly successful with products they genuinely love to use. We move with urgency and believe great outcomes come from high standards. About the role Hiring is one of the most consequential decisions a company makes. 3,000+ enterprises rely on HackerRank to get it right. We are now reinventing how that works for the agentic era. The integrity of that system is not a feature. It is the foundation. Open Problem The fraud gets smarter every quarter. The models have to keep up. Integrity isn't about whether you use AI or not; it's about whether you are following the rules. Our integrity system is a portfolio of signals spanning vision, code analysis, browser telemetry, and behavioral sequences. Each signal is independently trained, operates on a different modality, and has its own precision-recall tradeoff and failure distribution. That heterogeneity is not a
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