Red-Teaming QA Lead

Role overview

This is a remote contractor position paid on an hourly basis. The selected person will lead quality assurance for AI red-teaming and safety-evaluation work, with responsibility for maintaining consistency, reviewing trainer and QA output, and ensuring the work meets project standards.

You will examine AI-generated safety evaluations, adversarial prompts, risk assessments, and reviewer output; compare them against project rubrics; and provide clear written guidance that helps contributors improve. The role is designed for someone with strong judgment in AI safety and red-teaming, excellent English communication, and a highly organized approach to managing quality across distributed expert teams.

The company is a growing AI data services provider supporting training-data needs for major AI organizations and foundation-model labs. In this role, your quality leadership will help produce realistic, nuanced, policy-aware, and well-documented safety data that can surface model vulnerabilities more effectively.

Selection includes an AI interview, a domain-specific task, and a recruiter interview. There is no immediate project available for this position; however, qualified candidates may be contacted first when relevant work becomes available and may gain access to future projects through the expert network.

Key responsibilities

• Review red-teaming submissions, identify quality gaps, and send timely feedback through direct messages while escalating repeated or serious concerns.
• Assess adversarial prompts, model outputs, risk labels, safety analyses, policy interpretations, and vulnerability reports for realism, accuracy, and usefulness.
• Keep trainers and QAs informed on Discord about guideline changes, workflow updates, quality expectations, and red-teaming review standards.
• Answer questions from trainers and QAs promptly and clearly, especially on risk categories, attack strategies, policy boundaries, edge cases, severity, and rubric use.
• Re-engage inactive contributors, track follow-ups, and flag availability issues when participants are not consistently active.
• Build and maintain project documentation such as style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding resources.
• Run onboarding and training calls to walk trainers and QAs through expectations, workflows, rubrics, and review requirements.
• Make sure reviewers apply red-teaming and safety standards consistently as the project changes over time.
• Identify unsafe, weak, unrealistic, poorly documented, or policy-misaligned red-team items and surface them for correction.
• Recommend process improvements and help establish scalable QA workflows for AI red-teaming programs.

Candidate profile

Applicants should have a bachelor’s degree, master’s degree, or equivalent professional experience in fields such as Computer Science, Cybersecurity, AI Safety, Trust & Safety, Public Policy, Psychology, Linguistics, Law, Security Studies, Risk Analysis, or a related area.

A strong command of English is required for understanding instructions, coordinating with teams, and writing concise feedback.

At least 3 years of experience in AI safety, red-teaming, cybersecurity, trust and safety, content policy, risk analysis, adversarial testing, model evaluation, content moderation, or similar work is expected.

Experience with AI risk categories, adversarial prompting, jailbreak patterns, harmful-content taxonomies, misuse scenarios, policy interpretation, model behavior, and safety evaluation methods is important. Familiarity with prompt injection, social engineering, cybersecurity abuse, fraud, self-harm safety, extremist content, misinformation, privacy risk, illicit behavior, bias, and refusal behavior is preferred.

Prior experience leading or supporting remote teams of red-teamers, reviewers, policy analysts, annotators, researchers, or QAs is strongly preferred.

You should be comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management platforms.

High attention to detail and strong organization are needed, along with the ability to maintain style guides, FAQs, trackers, onboarding content, calibration tasks, and documentation.

Experience in AI training, LLM evaluation, safety evaluation, content moderation QA, policy QA, or rubric-based review will be considered a strong advantage.

Additional details

This role is a contract assignment and is remote-based. It is described as hourly work, and the source does not specify a fixed salary amount.

No opening count, start date, or application deadline is provided in the source information.

The listing does not mention a stipend, paid internship duration, or immediate project start.

Recruitment is handled through an AI interview, a task tailored to the role, and a recruiter interview.

Because this is a quality leadership role for AI red-teaming, the work focuses on safe, policy-aligned, realistic, and well-structured evaluation output.