Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will develop prompt-optimization and system-prompt-learning methods for agents. You will create methods for hypothesis testing and self-improvement, improve learning efficiency for long-running agentic tasks, run scalable experiments, and publish research through papers, reports, blog posts, and open-source code.
Requirements
- Machine learning
- LLM
- Continual learning
- Agent framework
- Prompt optimization
- Impactful research
- Publications
- Open-source contributions
- Empirical rigor
- Real-world impact
Responsibilities
- Develop prompt-optimization and system-prompt-learning methods for continuous learning from long-horizon tasks
- Develop methods for agents to generate and test hypotheses and improve their weaknesses
- Improve learning efficiency for long-running agentic tasks
- Design and run scalable experiments to measure self-improvement
- Publish research through papers, technical reports, blog posts, and open-source code
Hiring Process
Initial screen (30 min) → technical screen (1–1.5 hours) → paid in-person work trial (2 days onsite in San Francisco).