Research Engineer / Scientist, Self-Improvement
Develops methods for AI agents to self-improve post-training through prompt optimization, continual learning from long-horizon tasks, hypothesis testing, and scalable experiments. Requires expertise in LLMs, agent frameworks, and impactful research track record.
About the job
Responsibilities
- Developing prompt optimization and system prompt learning methods that allow agents to continuously learn from long-horizon tasks
- Developing methods allow agents automatically generate and test out hypotheses in an environment, or seek out and improve their own weaknesses
- Improving the efficiency of learning from long-running agentic tasks
- Designing and running experiments to measure self-improvement scalably
Requirements
- Expertise in machine learning, in particular LLMs and continual learning
- Familiarity with agent frameworks and prompt optimization
- Track record of impactful research (breakthrough publications and/or open-source contributions)
- Ability to balance execution speed with empirical rigor
- Real-world impact beyond pure academic work
Skills
LLMs, Continual Learning, Agent Frameworks, Prompt Optimization, Machine Learning, Open-Source, Experiments, Hypothesis Testing, Prompt Engineering
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