AI Safety, Red Teaming Specialist

Job not on LinkedIn

🕒 July 3

🌐 India, United States – Remote

🇮🇳 India – Remote 🇺🇸 United States – Remote

💵 $50 - $90 / hour

⏳ Contract/Temporary

🟢 Junior

🟡 Mid-level

🤖 Artificial Intelligence

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

💼 Consulting

👥 HR Tech

☁️ SaaS

Consulting • HR Tech • SaaS

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• Evaluate and strengthen the safety, security, and robustness of next-generation AI systems. • Leverage expertise in adversarial AI, LLM security, and AI safety to identify vulnerabilities. • Design evaluation methodologies and improve the resilience of large language models against real-world threats. • Collaborate with technical teams to translate security findings into actionable recommendations for AI safety improvements. • Document testing methodologies, findings, and best practices through clear technical reports and presentations for both technical and non-technical stakeholders.

🎯 Requirements

• 2+ years of experience in AI Safety, Adversarial Machine Learning, LLM Red Teaming, AI Security, or a related field. • Hands-on experience researching, testing, or identifying vulnerabilities involving prompt injection, ethical jailbreaks, adversarial attacks, or tool-use exploitation. • Strong understanding of modern LLM architectures, prompt engineering, and AI safety evaluation methodologies. • Experience developing structured security assessments, regression testing frameworks, and adversarial evaluation strategies. • Excellent analytical, documentation, and communication skills with the ability to explain complex technical findings clearly. • Ability to collaborate effectively within cross-functional technical teams. • Master's or PhD in Computer Science, Cybersecurity, Machine Learning, Artificial Intelligence, or a related discipline is preferred. • Contributions to AI security research, open-source AI safety tools, conference presentations, or published research are a plus. • Experience with AI model evaluation frameworks, prompt engineering techniques, and AI security assessment tools is advantageous. • Background in multidisciplinary AI safety, cybersecurity, or adversarial machine learning projects is beneficial.