Vercel
Software Engineer, Trust & Safety
About this role
Vercel is seeking a Software Engineer to build and operate trust and safety systems that protect millions of developers from abuse at scale. You'll analyze threat patterns, implement LLM-driven detection techniques, and develop production systems to mitigate emerging security threats across the platform.
What you'll do
- Analyze threat actor behavior and abuse patterns across large datasets to inform detection logic and mitigations
- Research and implement LLM-driven techniques for abuse detection, classification, and prevention
- Design and develop production-ready systems that detect and disrupt abusive behavior
- Build and maintain internal tools, datasets, and evaluation pipelines for trust and safety workflows
- Collaborate with security, product, and infrastructure teams to deploy research into stable systems
- Own systems end-to-end from research through production deployment and iteration
What they're looking for
- JavaScript/TypeScript programming
- Python programming
- Large language models (LLMs) and prompt engineering
- LLM evaluation and fine-tuning
- AWS cloud infrastructure
- Abuse prevention and trust & safety systems
- Threat actor behavior analysis
- Security engineering
Benefits
- Competitive compensation with equity
- Inclusive healthcare package
- Mentorship and professional development opportunities
- Flexible time off
- Equipment and work-from-home budget
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Vercel
Vercel builds a global content delivery platform and developer infrastructure centered on modern web frameworks like Next.js, along with AI-powered workflow and agent platforms. The company is hiring software engineers across infrastructure, full-stack, open-source, and AI teams to work primarily with TypeScript, Go, React, and Lua on performance optimization, developer tools, and intelligent automation features.
- Website
- vercel.com
Likely interview questions
- Tell us about a time you identified and mitigated an emerging abuse pattern—how did you approach the analysis and what was your impact?
- Describe your experience fine-tuning or evaluating LLMs in a production abuse detection context. What challenges did you face?