Compliance Engineer Intern

About the Company

Born from groundbreaking research at Columbia University and Yale University, CertiK is a leading Web3 security company focused on securing blockchain protocols, smart contracts, and decentralized applications through cutting-edge security research, formal verification, and AI-powered technology. Founded in 2017 and headquartered in New York City, CertiK provides end-to-end security solutions including smart contract audits, penetration testing, on-chain monitoring, incident response, and compliance services for some of the largest projects in the digital asset ecosystem.

Today, CertiK supports thousands of enterprise clients and Web3 projects globally, with a distributed international team spanning North America, Asia, and Europe. The company is backed by leading investors including Coatue, Goldman Sachs, Insight Partners, and Sequoia Capital, and has been recognized by organizations such as the World Economic Forum and CB Insights for its contributions to blockchain security innovation.

About You

You are a detail-oriented self-starter with a strong interest in regulatory compliance, digital assets, and AI. You are comfortable researching unfamiliar regulatory topics, organizing complex information, and producing clear first drafts. You are eager to learn how licensing applications and compliance reviews work in practice and can support both structured audit requests and fast-changing client-facing needs.

About the Role

You will support the Engineering team in regulatory research, licensing analysis, AI output review, and client-deliverable preparation. You will work with client-facing teams, compliance reviewers, and Engineering to organize regulatory knowledge, prepare initial drafts, and convert recurring compliance work into reusable mappings, templates, and workflows. All material regulatory conclusions and client deliverables will be reviewed by an experienced compliance professional.

Responsibilities

• Research and summarize regulatory frameworks and licensing requirements.
• Support licensing and regulatory-readiness projects, including applicability research, gap analysis, evidence requests, policy review, and application-document preparation.
• Organize regulatory requirements into structured modules, evidence categories, checklists, and mappings.
• Review AI outputs for factual consistency, source traceability, missing information, and formatting issues.
• Prepare initial drafts of compliance analyses, evidence guidance, client reports, pitch-deck content, and review materials.
• Support the maintenance of regulation-to-service mappings, evidence checklists, and submission-material reuse mappings.
• Organize customer-provided materials and identify potential evidence gaps for reviewer confirmation.
• Record reviewer feedback and update templates, evaluation cases, and compliance knowledge assets.
• Support coordination and request tracking across client-facing teams, compliance reviewers, and Engineering.
• Escalate unclear regulatory interpretations and unsupported conclusions to experienced reviewers.

Requirements

• Current enrollment in or recent graduation from a program in law, finance, accounting, risk management, public policy, business, computer science, or a related field.
• Strong research, analytical, and structured-writing skills.
• Strong attention to detail, particularly when reviewing regulatory sources, citations, and customer materials.
• Ability to organize complex information into clear checklists, summaries, presentations, and reports.
• Ability to manage multiple assignments and meet deadlines.
• Strong written and spoken English; professional Chinese proficiency is preferred but not required.
• Interest in digital-asset regulation, financial compliance, licensing, or AI-assisted compliance workflows.
• Familiarity with digital-asset regulation, VASP licensing, or similar regulatory frameworks is preferred.
• Previous internship, coursework, or project experience in compliance, law, audit, fintech, digital assets, or regulatory research is preferred.
• Basic understanding of LLM limitations, hallucination risk, source traceability, and human-in-the-loop review is preferred.