Applied AI Researcher, System Self-Improvement
Distyl AISan Francisco$150k – $250kPosted 26 March 2026
Tech Stack
Job Description
Applied AI Researcher, System Self-Improvement
ABOUT DISTYL AI
Distyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.
We research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.
Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.
WHAT WE ARE LOOKING FOR
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.
KEY RESPONSIBILITIES
- 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.
WHO YOU ARE
- 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.).
- 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 WE OFFER
- The base salary range for this role is $150K – $250K, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive ... (truncated, view full listing at source)
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