AI Safety Research Intern-2

Centific is a frontier AI data foundry that empowers clients with safe, scalable AI deployment. The AI Safety Research Intern will focus on advancing AI safety, designing and evaluating attack and defense strategies for LLM jailbreaks, and contributing to the platform's security guarantees through high-impact experiments.

Responsibilities

• Advance AI Safety: Design, implement, and evaluate attack and defense strategies for LLM jailbreaks (prompt injection, obfuscation, narrative red teaming)
• Evaluate AI Behavior: Analyze and simulate human-AI interaction patterns to uncover behavioral vulnerabilities, social engineering risks, and over-defensive vs. permissive response tradeoffs
• Agentic AI Security: Prototype workflows for multi-agent safety (e.g., agent self-checks, regulatory compliance, defense chains) that span perception, reasoning, and action
• Benchmark & Harden LLMs: Create reproducible evaluation protocols/KPIs for safety, over-defensiveness, adversarial resilience, and defense effectiveness across diverse models (including latest benchmarks and real-world exploit scenarios)
• Deploy and Monitor: Package research into robust, monitorable AI services using modern stacks (Kubernetes, Docker, Ray, FastAPI); integrate safety telemetry, anomaly detection, and continuous red-teaming
• Jailbreaking Analysis: Systematically red-team advanced LLMs (GPT-4o, GPT-5, LLaMA, Mistral, Gemma, etc.), uncovering novel exploits and defense gaps
• Multi-turn Obfuscation Defense: Implement context-aware, multi-turn attack detection and guardrail mechanisms, including countermeasures for obfuscated prompts (e.g., StringJoin, narrative exploits)
• Agent Self-Regulation: Develop agentic architectures for autonomous self-check and self-correct, minimizing risk in complex, multi-agent environments
• Human-Centered Safety: Study human behavior models in adversarial contexts—how users probe, trick, or manipulate LLMs, and how defenses can adapt without excessive over-defensiveness

Skills

• Ph.D. student in CS/EE/ML/Security (or related); actively publishing in AI Safety, NLP robustness, or adversarial ML (ACL, NeurIPS, BlackHat, IEEE S&P, etc.)
• Strong Python and PyTorch/JAX skills; comfort with toolkits for language models, benchmarking, and simulation
• Demonstrated research in at least one of: LLM jailbreak attacks/defense, agentic AI safety, human-AI interaction vulnerabilities
• Proven ability to go from concept → code → experiment → result, with rigorous tracking and ablation studies
• Experience in adversarial prompt engineering, jailbreak detection (narrative, obfuscated, sequential attacks)
• Prior work on multi-agent architectures or robust defense strategies for LLMs
• Familiarity with red-teaming, synthetic behavioral data, and regulatory safety standards
• Scalable training and deployment: Ray, distributed evaluation, CI/telemetry for defense protocols
• Public code artifacts (GitHub) and first-author publications or strong open-source impact

Company Overview

Company H1B Sponsorship