Remote RegTech Paralegal & ML QA Analyst

Cybernetic Controls Limited; United States; Remote; USD 8,500 - 13,000; Full time

Benefits offered by this job

25 days holiday + UK bank holidays

Healthcare contribution

Annual pay review

Opportunity to expand skills via paid/

Monthly Socials

Company Laptop

Job summary

Cybernetic Controls Limited (CCL) is a remote-first company delivering digital regulatory technology for the FinTech sector. We seek an entry-level paralegal or law graduate to review regulatory content, validate machine learning classifications, and ensure our rulebooks align with authoritative sources.

You’ll be the human judgment that keeps the model honest, working at the intersection of legal material and data pipelines.

Qualifications

• Required: law degree or paralegal qualification or equivalent legal training.
• Exposure to financial services regulation, RegTech, or legal/compliance contexts.
• Strong attention to detail and the ability to spot minor issues.
• Discipline to follow structured review and QA processes with clear records.
• Strong written communication usable by lawyers and engineers.

Responsibilities

• Review regulatory changes: monitor, assess impact, and ensure platform captures changes accurately.
• Validate ML topic modelling: check classifications and provide feedback for model improvement.
• Check rulebook structure: compare against original sources; flag gaps or errors.
• Build the service: liaise with client requests, QA requirements, and engineering for delivery.

Skills

Law basics

Regulatory exposure

Attention to detail

Structured QA

Clear written comms

Education

Law degree or paralegal qualification

Tools

Jira

Confluence

Job description

Cybernetic Controls Limited (CCL) is a remote-first company delivering digital regulatory technology for the FinTech sector. We seek an entry-level paralegal or law graduate to review regulatory content, validate machine learning classifications, and ensure our rulebooks align with authoritative sources.

You’ll be the human judgment that keeps the model honest, working at the intersection of legal material and data pipelines.