OpenAISan Francisco, CAResearcher developing evaluations, red-teaming pipelines, and novel mitigations for frontier AI safety risks. The role requires deep technical expertise, research engineering experience, advanced training in computer science or machine learning, and proficiency in Python or similar languages.
Conduct strategic, technically rigorous research to anticipate and mitigate loss-of-control risks from increasingly capable AI systems, including recursive self-improvement. The role combines hypothesis-driven research, rapid prototyping, safety evaluations, monitoring, and institutionalizing effective interventions.
Develop AI systems that automate and accelerate research by designing evaluations, building research agents and orchestration infrastructure, and improving model capabilities through training and synthetic data. The role suits strong research or engineering generalists experienced with LLMs, evaluations, agents, infrastructure, or distributed systems.
Researches and develops memory and personalization improvements for frontier models through post-training, reinforcement learning, dataset creation, and evaluations. The role requires strong machine-learning expertise, research craftsmanship, and the ability to work across a large codebase with research and product teams.
Conducts research to improve the safety of multimodal AI systems spanning text, vision, and audio. The role requires experience building multimodal models, post-training frontier systems, designing safety evaluations, and translating research findings into reliable model behavior.
Develops novel reinforcement learning techniques using synthetic environments and feedback to enhance large-scale AI models. Designs experiments, analyzes dynamics, and integrates research into production systems; requires strong RL/ML background and engineering skills.