Advance AI Safety: Design, implement, and evaluate attack and defense strategies for LLM jailbreaks (prompt injection, obfuscation, narrative red teaming) and deploy them as production-grade services.
Build Scalable Safety Infrastructure: Architect and deploy distributed safety evaluation pipelines handling millions of requests, with real-time monitoring, alerting, and incident response capabilities.
Large-Scale Data Engineering: Design ETL pipelines for processing terabytes of safety-related data (attack patterns, behavioral logs, model outputs); build data lakes and feature stores for safety ML systems.
Evaluate AI Behavior: Analyze and simulate human-AI interaction patterns at scale to uncover behavioral vulnerabilities, social engineering risks, and over-defensive vs. permissive response tradeoffs.
Agentic AI Security: Build production workflows for multi-agent safety (agent self-checks, regulatory compliance, defense chains) spanning perception, reasoning, and action.
MLOps & Model Deployment: Deploy safety models to production using containerized microservices, implement CI/CD pipelines for model updates, and manage model versioning and A/B testing infrastructure.
Benchmark & Harden LLMs: Create reproducible, automated evaluation protocols for safety, over-defensiveness, and adversarial resilience across diverse models with continuous integration.

Master's degree in CS/EE/ML/Security or related field (Ph.D. preferred)
2+ years of industry experience in applied ML/AI research or ML engineering
Track record of publications in AI Safety, NLP robustness, or adversarial ML (ACL, NeurIPS, ICML, EMNLP, IEEE S&P, etc.) or equivalent applied research impact
Strong Python and PyTorch/JAX skills with experience deploying ML models to production
Demonstrated experience in at least one of: LLM jailbreak attacks/defense, agentic AI safety, adversarial ML, or human-AI interaction vulnerabilities
Experience with containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, or Azure)
Proven ability to take research from concept to code to production deployment with rigorous testing and monitoring.

Real Impact: Your research ships directly, securing our core features and AI infrastructure at scale
Research to Production: Bridge the gap between cutting-edge research and production systems
Mentorship: Collaborate with Principal Architects and senior researchers in AI safety and adversarial ML
Velocity + Rigor: Balance high-quality research with mission-critical product focus

AI Engineer – Responsible AI

Key skills