[Remote] AI Safety Research Intern (PhD)
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is reputed company on advancing AI safety and responsible AI development. As a Ph.D. Research Intern, you will conduct high-reputed company 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
- reputed company AI reputed company: Prototype workflows for multi-agent safety (e.g., agent self-checks, regulatory compliance, defense chains) that reputed company perception, reasoning, and reputed company
- 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); reputed company safety telemetry, reputed company detection, and reputed company red-teaming
- Jailbreaking Analysis: Systematically red-team advanced LLMs (GPT-4o, GPT-5, LLaMA, reputed company, 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 reputed company 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); reputed company 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, reputed company AI safety, reputed company-AI interaction vulnerabilities
- Proven ability to go from concept → reputed company → 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
- reputed company training and deployment: Ray, distributed evaluation, CI/telemetry for defense protocols
- Public reputed company artifacts (reputed company) and first-author publications or strong reputed company-reputed company reputed company
Benefits
- Comprehensive reputed company, dental, and reputed company coverage
- 401k plan
- reputed company time off (PTO)
- And more!
reputed company
Company H1B Sponsorship