Red-Teaming Quality Assurance Lead

The work

You will oversee quality and consistency across AI red-teaming and safety evaluation projects. You will review work from trainers and quality reviewers, then give clear written feedback using project guidelines and rubrics.

The role also includes keeping remote contributors aligned, identifying repeated quality issues, and supporting the processes that help safety evaluation projects run consistently.

• Review adversarial prompts, model responses, risk classifications, safety analyses, policy explanations, and vulnerability reports.
• Assess accuracy, realism, usefulness, adversarial reasoning, policy awareness, safety taxonomy alignment, scenario realism, vulnerability coverage, formatting, instruction-following, and rubric adherence.
• Spot-check trainer and QA submissions, escalate recurring issues, and reinforce quality standards across projects.
• Share workflow changes, review expectations, and other updates with trainers and QAs through remote collaboration channels.
• Support onboarding, documentation, FAQs, trackers, calibration tasks, and process improvements.
• Help manage contributor activation and follow up when team members are inactive or working inconsistently.

What it pays and takes

This is a remote, part-time contract role for an expert-level contributor. The work requires strong written English, careful review against detailed rubrics, and experience with AI safety or related evaluation and risk workflows.

• Pay: $100 per hour.
• Time: 20 or more hours per week.
• Location: Open to candidates in the United States.
• Work setup: Remote contractor role.
• Language: Strong English communication skills.
• Required experience: AI safety, red-teaming, cybersecurity, trust and safety, content policy, risk analysis, model evaluation, content moderation, or related work.
• Required knowledge: Adversarial prompting, jailbreak patterns, harmful-content taxonomies, misuse scenarios, policy interpretation, and safety evaluation principles.
• Team skills: Comfort leading or supporting remote teams and evaluating work against detailed rubrics.
• Tools: Experience with Discord, Google Sheets, Google Docs, trackers, dashboards, or project management systems.

Helpful background

Relevant backgrounds include computer science, cybersecurity, AI safety, trust and safety, public policy, psychology, linguistics, law, security studies, or risk analysis. Experience with AI training, large language model evaluation, safety evaluations, content moderation QA, policy QA, or rubric-based review is also useful.

• Familiarity with prompt injection and social engineering.
• Understanding of cybersecurity abuse, fraud, self-harm safety, extremist content, misinformation, privacy risk, and illicit behavior.
• Knowledge of bias and model refusal behavior.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training is the human work behind modern AI systems. People review prompts and model responses, prepare examples, and evaluate safety so models can handle real requests more reliably.