Abnormal

Staff Software Engineer, Security & Privacy

$210K–$302KFull-time · Remote - USA
✓ Verified live on the employer's own system · added 138 days ago
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Senior

Requirements

Education: Bachelor's degree

Skills & tools

Distributed SystemsSecurityTeam LeadershipElectricalArchitecture PatternsMachine LearningRecordkeeping
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Full job description

Abnormal AI is seeking a Staff Software Engineer to join the Multi-Product Platform division, owning Security & Privacy as a platform capability.

We're looking for a distributed systems expert who has worked closely with security teams - someone who can drive Product Security at scale and translate that into clear, executable technical roadmaps.

- First-principles thinker - you break complex, ambiguous problems down to fundamentals, then rebuild them into clear, prioritized milestones that deliver real customer value.

- Distributed systems expert - you bring deep experience designing and operating large-scale distributed systems, and have partnered with security teams to build secure-by-default solutions.

- Roadmap-to-reality executor - you transform requirements into actionable technical roadmaps and lead large-scale projects from concept to production.

- AI-native innovator - you bring expertise and creativity in applying AI-native approaches to unlock new capabilities.

- Drive end-to-end delivery of large-scale, cross-functional Security & Privacy initiatives - keeping pace with product expansion while preserving R&D velocity.

- Draw the Security & Privacy roadmap for the Multi-Product Platform division across a two year horizon, partnering with engineering, product, and business stakeholders.

- Mentor and grow the next generation of Tech Leads, raising the technical bar across the organization.

- BS degree in Computer Science, Electrical Engineering, Computer Engineering or other related engineering field.

- Deep expertise in distributed systems, large-scale system design and architectural patterns.

- Demonstrated track record of owning end-to-end delivery of complex, cross-functional projects.

- Experience across multiple domains - infrastructure, data platform, machine learning, or similar.

- Contributions to open-source projects, engineering blogs, or industry standards.

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