Cybersecurity SWE - AI Safety
Apply cybersecurity software engineering skills to evaluate and harden AI systems against security risks and unsafe outputs.
Apply cybersecurity software engineering skills to evaluate and harden AI systems against security risks and unsafe outputs.
Apply advanced biology expertise to evaluate AI outputs and improve model safety on biological reasoning and sensitive knowledge.
Use advanced chemistry expertise to evaluate AI outputs and strengthen model safety on chemical reasoning and hazardous knowledge.
Lead red-teaming quality assurance efforts, identifying vulnerabilities and safety risks in AI model outputs through adversarial evaluation.
Design comprehensive AI training scenarios and test cases to improve model behavior and safety alignment.
Review AI training scenarios for quality, accuracy, and alignment to ensure high-quality training data standards.
Rate and validate axis sanity checks to improve AI model reasoning and evaluation quality.
Evaluate personalization quality in AI systems using cultural and contextual understanding of Japanese user behavior.
Design and craft multi-turn conversational datasets to improve AI agent reasoning, function calling accuracy, and real-world interaction capabilities.
Improve AI safety systems by reviewing Spanish-language content.
Train AI moderation models using Portuguese-language content.
Review Korean-language content to improve AI moderation and safety systems.
Train AI moderation systems using Japanese content to detect harmful or policy-violating material.
Enhance AI safety by reviewing Italian-language content and training moderation systems.
Improve AI safety models by reviewing Hebrew content and identifying policy violations.
Train AI systems to detect harmful German-language content and improve moderation accuracy.
Analyze French content to train AI moderation systems and enhance safety detection.
Improve AI moderation systems by reviewing Chinese-language content for safety and compliance.
Train AI moderation systems using Arabic content by identifying harmful patterns and improving safety model accuracy across global platforms.
Evaluate LLM behavior and validate large-scale code repositories.
Evaluate and improve large language models using strong software engineering expertise across multiple programming languages.
Remote red-teaming role focused on probing AI systems for vulnerabilities, misalignment, and safety issues. Help design and execute adversarial prompts and edge cases to improve AI robustness.
Load more · 22 remaining AI safety expertise is crucial for ensuring artificial intelligence systems behave responsibly and reliably. AI safety training projects rely on human reviewers to identify risks, evaluate unsafe outputs, test edge cases, and guide AI alignment with human values. Without expert oversight, AI systems cannot be deployed safely at scale.
AI safety freelance jobs transform analytical and ethical expertise into high-impact AI training work. Professionals working in AI safety and alignment help improve large language model behavior, reduce bias, and prevent harmful responses. These AI safety projects are remote, well-compensated, and essential to the future of responsible AI development.
Live ai safety projects on PARA AI Labs currently pay between $30/hr and $150/hr, with rates listed upfront on every listing. Specialist experience earns the upper end of the range.
Mercor, SME Careers, Turing, Mindrift currently list ai safety projects through PARA AI Labs. Each Apply link goes directly to the hiring platform.
Click Apply on any project below — you'll go straight to the source platform's application, with no middlemen and no fees. Most platforms ask for a short skills assessment before matching you to paid work.
Join thousands of professionals earning from AI training jobs worldwide.