Applied AI Researcher, System Self-Improvement
Designs feedback-driven AI architectures for system self-improvement, enabling autonomous evolution through reflection, retraining, and adaptation. Requires proven research in compound AI systems, daily AI tool usage, and strong prototyping skills.
About the job
Key Responsibilities
- Design feedback-driven systems capable of detecting weaknesses, generating corrective hypotheses, and implementing improvements through reflection, retraining, or workflow adaptation.
- Build AI systems that compound in capability through use.
- Study how systems form self-models to understand success/failure.
- Explore reflective reasoning, reward modeling, self-evaluation techniques, reinforcement learning, interpretability, and meta-optimization for continuously learning enterprise systems.
Who You Are / Requirements
- Experience building feedback-driven systems (evaluators, retrievers, iterative refinement loops, performance dashboards).
- Expertise in compound AI systems, agentic collaboration (ensembling, ReAct, graph-of-thoughts).
- Proven research track record (publications, public work).
- Daily use of AI tools (ChatGPT, Cursor, Perplexity).
- Strong programming and data analysis skills for prototyping and experiments.
What We Offer
- Base salary: $150K – $250K.
- Equity, comprehensive benefits (100% covered medical/dental/vision, 401(k), perks).
Skills
Reinforcement Learning, Interpretability, Meta-Optimization, React, Graph-Of-Thoughts, Reward Modeling, Reflective Reasoning, Agentic Collaboration, Ensembling, Self-Evaluation
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