Applied AI Researcher (System Self-Construction)

Requirements

• 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,
• Experience Building Modular and Composable Systems: You’ve engineered architectures where components interact dynamically (e.g. graph-based planners, agent orchestration frameworks, workflow engines, or automated pipeline builders),
• 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.),
• 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,
• 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,
• 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,
• 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

What the job involves

• 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-Construction team builds systems that can build other systems. Researchers design architectures capable of autonomously generating, assembling, and refining sub-systems,
• This involves developing meta-learning loops, automated pipeline generation, and self-architecting frameworks that reduce the human bottleneck in complex system creation,
• Researchers in Self-Construction study recursive design principles: how systems represent their own construction processes, evaluate them, and evolve,
• They draw from meta-learning, program synthesis, and evolutionary computation to explore what it means for a system to “understand” and redesign itself. Their work defines the frontier of self-referential, generative system architectures