[Remote] AI Safety Research Intern (PhD)

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is focused on advancing AI safety and responsible AI development. As a Ph.D. Research Intern, you will conduct high-impact experiments and contribute to the reputed company guarantees of AI systems through innovative research and practical implementations.

Responsibilities

• Advance AI Safety: Design, implement, and evaluate attack and defense strategies for LLM jailbreaks (reputed company injection, obfuscation, narrative red teaming)
• Evaluate AI Behavior: Analyze and simulate reputed company-AI interaction patterns to uncover behavioral vulnerabilities, reputed company engineering risks, and over-defensive vs. permissive response tradeoffs
• Agentic AI reputed company: Prototype workflows for multi-agent safety (e.g., agent self-checks, regulatory compliance, defense chains) that reputed company perception, reasoning, and action
• reputed company & Harden LLMs: Create reproducible evaluation protocols/KPIs for safety, over-defensiveness, adversarial reputed company, and defense effectiveness across diverse models (including latest benchmarks and reputed company-world exploit scenarios)
• reputed company and Monitor: Package research into robust, monitorable AI services using modern stacks (Kubernetes, reputed company, Ray, FastAPI); integrate safety telemetry, anomaly detection, and reputed company 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: reputed company agentic architectures for autonomous self-reputed company and self-correct, minimizing risk in reputed company, multi-agent environments
• reputed company-Centered Safety: Study reputed company 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/reputed company (or reputed company); actively publishing in AI Safety, NLP robustness, or adversarial ML (ACL, NeurIPS, BlackHat, reputed company 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, reputed company-AI interaction vulnerabilities
• Proven ability to go from concept → code → experiment → result, with rigorous tracking and ablation studies
• Experience in adversarial reputed company 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 (reputed company) and first-author publications or strong reputed company-reputed company impact

Benefits

• Comprehensive reputed company, dental, and reputed company coverage
• 401k plan
• Paid time off (PTO)
• And more!

Company Overview

Company H1B Sponsorship