AI safety is one of the fastest-growing fields in tech right now. 24 Mag is hiring a Remote Telugu-English AI Safety Red Team Evaluator paying between $20 and $30 per hour. This part-time consulting role targets bilingual professionals who can stress-test AI systems, classify risks, and document findings with precision.
The position is fully remote and open to candidates across the United States. It sits at the intersection of language expertise and AI safety evaluation. Understanding what skills this role demands can help you assess your readiness honestly.
What Is a Red Team Evaluator in AI Safety?
Red teaming comes from cybersecurity. In that world, red teamers simulate attacks to expose vulnerabilities before bad actors find them. AI red team evaluators do something similar but with language models. They craft adversarial prompts, probe model behavior, and identify where an AI might produce harmful, biased, or misleading outputs.
This role at 24 Mag focuses specifically on Telugu and English language contexts. That bilingual angle is critical. Many AI safety teams lack coverage in regional languages, which creates blind spots in model behavior across different cultural and linguistic scenarios.
Telugu-English Bilingual Proficiency
The most non-negotiable skill for this role is native or near-native fluency in both Telugu and English. You need to read, write, and evaluate AI outputs in both languages at a high level. Casual conversational Telugu is not enough here.
The role requires you to identify nuanced safety failures across languages. That means spotting bias, misinformation, or harmful content in Telugu-language AI outputs with the same sharpness you apply to English. Cultural context matters just as much as grammar.
Strong written English is equally important. All evaluation reports and documentation must be produced in clear, professional English. Your findings need to communicate risks to both technical and non-technical audiences effectively.
Technical Skills Required
AI and Machine Learning Fundamentals
You do not need to be a machine learning engineer for this position. However, a solid understanding of how large language models work is essential. Knowing concepts like prompt sensitivity, token limits, and model refusal behavior helps you design better adversarial tests.
Familiarity with conversational AI systems and multi-turn dialogue is also important. Many safety failures emerge not in single prompts but across extended conversations. Understanding how context builds across a conversation helps you find those weaknesses.
Adversarial Testing and Prompt Engineering
This is the core technical skill of the role. Adversarial prompt engineering means deliberately designing inputs to push AI models toward unsafe or unreliable outputs. This includes prompt injection, jailbreak attempts, and edge-case scenario construction.
- Crafting multi-turn adversarial conversation flows
- Designing prompts that test bias exploitation
- Simulating misuse scenarios across different user types
- Stress-testing model behavior on sensitive or restricted topics
Experience with structured testing frameworks is a strong advantage. Many professional red team roles use established playbooks and taxonomies to keep testing consistent and reproducible.
Vulnerability Classification and Risk Annotation
Classifying vulnerabilities is a distinct skill from finding them. Once you identify a safety failure, you need to categorize it accurately using established taxonomies. Common categories include hallucination risks, harmful content generation, bias amplification, and socio-technical misuse patterns.
Annotation work requires discipline and consistency. Applying the same classification standards across hundreds of test cases demands patience and a systematic approach. Errors in annotation can undermine the quality of the entire evaluation dataset.
Documentation and Reporting
Producing clear, reproducible evaluation artifacts is a major part of this job. That means writing structured reports, building test case datasets, and summarizing findings in ways that teams can act on. Vague or disorganized documentation reduces the value of even the best red team work.
You should be comfortable writing technical summaries that non-technical stakeholders can understand. Translating complex AI safety findings into plain language is a skill in itself. It requires both clarity of thought and strong writing discipline.
Soft Skills That Matter for This Role
Critical Thinking and Analytical Judgment
Structured critical thinking is what separates effective red teamers from those who just guess. You need to approach AI systems with deliberate skepticism, asking where and how they might fail rather than assuming they will perform correctly.
Analytical judgment comes into play during risk classification. Not every problematic AI output carries the same level of risk. Deciding what is a minor reliability issue versus a serious safety vulnerability requires careful, calibrated judgment.
Attention to Detail
Missing a subtle bias or a borderline harmful output can leave real safety gaps in an AI system. High attention to detail is non-negotiable in evaluation work. This applies to both the testing phase and the documentation phase equally.
Consistency matters too. Producing reliable human evaluation data means applying the same standards across every case you review. Even small inconsistencies can compromise the integrity of a dataset.
Communication Skills
Much of this role involves translating findings into actionable insights. Clear written communication is essential, especially when explaining risks to audiences with different technical backgrounds. Your reports need to be precise without being impenetrable.
Working remotely also means you communicate mostly through written channels. Being organized, direct, and responsive in written communication helps the whole project run smoothly.
Ethical Awareness and Cultural Sensitivity
Red team evaluators regularly engage with sensitive content, including harmful speech, misinformation, and potential misuse scenarios. Strong ethical grounding helps you navigate this content professionally without losing objectivity.
Cultural sensitivity is particularly important given the bilingual Telugu-English scope of this role. Understanding how harmful content or bias manifests differently across cultures makes your evaluations more accurate and more useful.
Experience That Strengthens Your Application
Prior experience in AI safety evaluation, content moderation, or NLP annotation is highly relevant here. Even academic or volunteer work in these areas demonstrates practical familiarity with the core tasks involved.
Backgrounds in linguistics, computational linguistics, or translation also transfer well. These fields build the analytical language skills that bilingual evaluation work demands. Experience working with structured data or annotation tools is another useful credential.
Red teaming or security testing experience from cybersecurity is relevant too. The mindset of probing systems for weaknesses translates directly into AI safety evaluation work. Any professional background that required systematic testing and documentation will be viewed favorably.
How to Build These Skills
Several platforms offer free or low-cost training in AI safety and red teaming concepts. Anthropic, OpenAI, and the Center for AI Safety all publish resources on AI safety evaluation frameworks. Reading these gives you grounding in how professionals approach the field.
Hands-on practice matters most. Spend time interacting with publicly available AI chatbots and deliberately probing their limits. Try prompt injection techniques, test sensitive topic boundaries, and document what you find. Building a personal portfolio of adversarial test cases demonstrates real capability.
For the annotation and documentation side, platforms like Scale AI, Surge AI, and Appen offer paid data labeling and evaluation work. These roles build the structured judgment and consistency habits that professional AI safety evaluation requires. Even a few months of this work creates meaningful experience.
Telugu language skills are harder to develop quickly if you are not already fluent. If you are a native speaker, focus instead on strengthening your ability to evaluate Telugu text critically for nuance, cultural context, and subtle harmful content. That applied analytical skill is what this role actually demands.
Apply for the 24 Mag Remote Telugu-English AI Safety Red Team Evaluator role here: https://himalayas.app/companies/24-mag/jobs/remote-telugu-english-ai-safety-red-team-evaluator-20-30-hour
