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Curated cybersecurity and AI/ML roles — engineering, research and security plus go-to-market and executive roles at cyber and AI companies. North America, every one vetted, never aggregated.
49 live roles · 100% vetted · refreshed hourly
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31 results in AI / ML
25–31 of 31Anthropic Research Resident
Anthropic · San Francisco, CA | New York City, NY
Opportunity to work on foundational AI safety research with Anthropic's world-class team under mentors like Nicholas Carlini and Jascha Sohl-Dickstein, with direct access to Claude models and research infrastructure.
ResearchVettedResearch Engineer
$250k–$445kOpenAI · San Francisco
Lead technical research at OpenAI, directly contributing to frontier AI systems and their safe deployment at global scale.
EngineeringVettedSoftware Engineer, AI Connections (Developer Platform)
Notion · San Francisco, California · Hybrid
Key platform engineer at a $10B+ AI-native workspace company, architecting foundational extensibility for millions of users and agents.
EngineeringVettedAI Engineer – Forward Deployed Engineering (AI FDE), U.S. Public Sector (Federal Focus)
Databricks · Maryland; Virginia; Washington, D.C.
Customer-facing AI specialist at Databricks shaping federal GenAI strategy and thought leadership while deploying cutting-edge LLMOps and ML systems at scale.
EngineeringVettedMachine Learning Research Scientist, Evaluations
Scale AI · San Francisco, CA; Seattle, WA; New York, NY
Lead evaluation-driven AI development at a company partnering directly with industry's leading AI labs to advance GenAI research at scale.
ResearchVettedAnthropic Fellows Program, AI Safety & Security
Anthropic · London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA · Remote
Direct mentorship from leading AI safety researchers with $15k/month compute budget and 80%+ past fellows publishing peer-reviewed work.
ResearchVettedResearcher, Safety Training
$380k–$500kOpenAI · San Francisco
Lead safety research on production AI systems at scale, translating frontier safety problems into concrete ML methods that shape model behavior across billions of users.
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