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Applied AI Researcher, System Self-Improvement

150k – 250kSan Francisco, CANew York, NYHybrid
Summary

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 role

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 learninginterpretabilitymeta-optimizationReActgraph-of-thoughtsreward modelingreflective reasoningagentic collaborationensemblingself-evaluation
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