Job Overview
We are seeking creative and analytical Content Adversarial Red Team Analysts to test AI platforms and models against content safety policies and compliance requirements.
In this role, you will design and execute challenging test scenarios to identify gaps in how AI systems respond to complex, unusual, or unexpected inputs. You will explore edge cases and different approaches to assess whether platforms and models consistently follow defined safety policies and compliance boundaries.
An ideal candidate is curious, analytical, and able to think from different user perspectives. You should have a strong understanding of content safety and policy and be comfortable exploring complex scenarios to identify weaknesses, inconsistencies, or gaps in system behaviour.
Your work will help identify areas of improvement and support the development of safer and more reliable AI systems.
Project Details
• Contract Type: Freelance, with the potential to convert to a full-time role.
• Pay Rate: US$13 per hour
• Location: India
• Language: English
Responsibilities
• Design and execute authorized adversarial test scenarios to evaluate AI systems against content safety policies and compliance requirements.
• Develop diverse and challenging prompts, inputs, and scenarios to test system behaviour.
• Explore edge cases, unusual inputs, and complex situations that may reveal gaps in safety controls or policy enforcement.
• Evaluate AI responses and identify potential weaknesses, inconsistencies, or policy-related concerns.
• Test how systems respond to different forms of language, context, intent, and user behaviour.
• Identify recurring patterns or scenarios that may require further testing or improvement.
• Document test scenarios, system responses, findings, and supporting evidence clearly.
• Apply defined testing guidelines and project requirements consistently.
• Review complex cases and use sound judgment when assessing system behaviour.
• Maintain high levels of quality, accuracy, and attention to detail while completing assigned tasks.
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