Ai Safety Specialist

Há 2 dias


Ituiutaba, Brasil Bebeeredteamer Tempo inteiro

We are seeking highly analytical and detail-oriented professionals to help us rigorously test and evaluate AI-generated content.
The ideal candidate will identify vulnerabilities, assess risks, and ensure compliance with safety, ethical, and quality standards.The key responsibilities of this role include conducting Red Teaming exercises to identify adversarial, harmful, or unsafe outputs from large language models (LLMs), evaluating and stress-testing AI prompts across multiple domains, and developing and applying test cases to assess accuracy, bias, toxicity, hallucinations, and misuse potential in AI-generated responses.This role requires collaboration with data scientists, safety researchers, and prompt engineers to report risks and suggest mitigations.
Additionally, manual QA and content validation across model versions is necessary to ensure factual consistency, coherence, and guideline adherence.The ideal candidate will have a strong background in Quality Assurance, content review, or test case development for AI/ML systems.
They should also have experience in AI red teaming, LLM safety testing, or adversarial prompt design, as well as familiarity with prompt engineering, NLP tasks, and ethical considerations in generative AI.A bachelor's degree in a relevant field, such as linguistics, psychology, or computational ethics, is preferred.
Prior work with teams like Open AI, Anthropic, Google DeepMind, or other LLM safety initiatives is also an asset.This role offers the opportunity to work on cutting-edge technology and contribute to the development of safe and responsible AI systems.
If you are passionate about AI safety and have the necessary skills and experience, we encourage you to apply for this exciting opportunity.Key Requirements:Proven experience in AI red teaming, LLM safety testing, or adversarial prompt designFamiliarity with prompt engineering, NLP tasks, and ethical considerations in generative AIStrong background in Quality Assurance, content review, or test case development for AI/ML systems


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