AI Alignment and Safety Engineering

Explore AI alignment theory, value learning, and corrigibility frameworks. Ideal for researchers designing safe, goal-aligned AI systems.

Navigate AI risk frameworks, responsible scaling policies, and governance structures to align organizational AI practices with safety standards.

Apply mechanistic interpretability and feature visualization techniques to understand what neural networks learn and how they make decisions.

Simulate adversarial attacks on AI systems to uncover safety failures, jailbreaks, and misuse vectors before deployment.

Build rigorous safety benchmarks and evaluation suites to measure AI model behavior across harm categories, capability thresholds, and alignment properties.

Draft AI safety policies, acceptable use frameworks, incident response protocols, and internal governance documents for AI-deploying organizations.

Study AI corrigibility, shutdown problems, and human control mechanisms to ensure AI systems remain safely interruptible and correctable.

Investigate mesa-optimization, deceptive alignment, and inner alignment failures in learned models to build safer training pipelines.

Design and evaluate reward models for RLHF pipelines, addressing reward hacking, proxy misalignment, and human preference learning.

Research protocols and architectures for maintaining meaningful human oversight of AI systems as they surpass human-level task performance.