Applied Artificial Intelligence Researcher (System Self-Improvement)

• 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
• The System Self-Improvement team builds architectures that continuously evaluate and enhance their own performance
• Researchers design feedback-driven systems capable of detecting weaknesses, generating corrective hypotheses, and implementing their own improvements through reflection, retraining, or workflow adaptation. The goal: build AI systems that compound in capability through use
• 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

Benefits

• Equity options and healthcare
• Collaborative and supportive work environment
• Strong focus on personal and professional growth- At Distyl we’re pushing the envelope of AI utilization in enterprise. This requires creative researchers who don’t just want to drive incremental improvements on benchmarks or optimize an existing process but instead are looking to creatively redefine how software is used
• 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
• 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
• 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
• 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
• 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.)
• 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