Applied AI Researcher, System Self-Construction

Distyl AI develops AI native technologies for humans & AI to collaborate to power the operations of the Global Fortune 1000.

In just 24 months, we’ve rapidly grown to partner with some of the world’s largest enterprises—including F100 telecom, healthcare, manufacturing, insurance, and retail companies—delivering multiple AI deployments with $100M+ impact. Our platform, Distillery, along with our team of AI Engineers, Researchers, and Strategists, is pioneering AI-native systems of work, solving the most complex, high-stakes challenges at scale.

Distyl is founded and led by proven leaders from companies like Palantir, Apple, and top national laboratories. We work in deep partnership with OpenAI, jointly going-to-market at the largest enterprises and collaborating evaluating and testing the latest models. Backed by Lightspeed, Khosla, Coatue, industry leaders like Nat Friedman (former Git Hub CEO), as well as board members of over 20+ F500s, Distyl is building the future of AI-powered enterprise operations.

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.

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.

• 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.

• 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…