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Distyl AI is looking to solve complex, high-stakes challenges at scale for Global Fortune 1000 companies by pioneering AI-native systems of work. The specific problem is to build AI systems that can compound in capability through use by continuously evaluating and enhancing their own performance.

Requirements

• Experience Building Feedback-Driven Systems: You’ve built or studied systems that monitor, score, and adapt—evaluators, retrievers, iterative refinement loops, or performance dashboards that close the feedback loop.
• Experience Building with Models, Not Just Building Models: We develop intelligent systems using models rather than training or fine-tuning them.
• Ideal candidates have expertise in compound AI systems, agentic collaboration, and associated techniques (ensembling, ReAct, graph-of-thoughts, etc.).
• Strong Programming and Data Analysis Skills: While you might not consider yourself a software engineer you need to be able to build prototypes of your ideas and then perform the experiments to prove the effectiveness to a F500 Head of AI.
• Uses AI Every Day: Before you can revolutionize someone else’s workflow, you need to revolutionize yours. You should be using tools like ChatGPT, Cursor, and Perplexity to accelerate your workflow.

Responsibilities

• Researchers design feedback-driven systems capable of detecting weaknesses, generating corrective hypotheses, and implementing their own improvements through reflection, retraining, or workflow adaptation.
• Researchers in Self-Improvement study how systems can form self-models—understanding when, why, and how they succeed or fail.
• They explore reflective reasoning, reward modeling, and self-evaluation techniques to enable autonomous evolution.
• This work bridges reinforcement learning, interpretability, and meta-optimization to pioneer continuously learning enterprise systems.
• You’ve built or studied systems that monitor, score, and adapt—evaluators, retrievers, iterative refinement loops, or performance dashboards that close the feedback loop.
• We develop intelligent systems using models rather than training or fine-tuning them.
• Ideal candidates have expertise in compound AI systems, agentic collaboration, and associated techniques (ensembling, ReAct, graph-of-thoughts, etc.).

Other

• Our researchers come from many academic backgrounds but have strong research track records, operate in an AI-native way, and would be bored staying on the rails of a traditional research org.
• Proven Track Record of Research Results: Whether you’ve published in top journals, posted amazing work on twitter, or somewhere else we want to see what you've done.
• Biases Towards Showing vs Telling: Our customers want to see the power of AI today vs discuss the most elegant idea that will take 5 years to realize.
• An opportunity to advance the cutting edge of LLM research and directly revolutionize work in the enterprise space.
• Ownership of high-impact research projects, with the autonomy to explore novel approaches and solutions.