Head of AI Research
As Head of AI Research, you will define and lead Kepler's research agenda, focusing on making AI trustworthy for enterprise decisions. This involves pioneering agentic architectures, advancing memory and retrieval systems, and building evaluation frameworks, with all research shipping directly to production to power high-stakes financial workflows.
High-stakes industries are falling behind on AI adoption. Their workflows can’t afford wrong answers, and AI can’t be trusted to give right ones because of hallucinations. The barrier isn’t that the models aren’t smart enough; it’s that no one can verify what they produce. The fix isn’t a better model, it’s a trust layer: every output traceable, every calculation auditable, every answer reproducible. Kepler is the agent harness – the infrastructure layer that wraps around AI models to make their outputs reliable, traceable, and verifiable. The model is a replaceable component; the harness is the product.
In Kepler's architecture, the LLM orchestrates – it decides what data to gather, what to compute, how to structure the output. But every actual data point, every extracted value, every calculation flows through deterministic code pipelines. The LLM never touches the data itself. Every value carries provenance metadata back to its exact source. Every computation is auditable and reproducible. Verification loops cross-check outputs before users ever see them.
We started in finance because the stakes are highest and the tolerance for error is zero. We’ve built a finance research product that lets analysts supercharge their workflow: pulling comparables, building models, and researching filings. No more double-checking every number AI spits out. Every number tracing back to the source, every time.
But the architecture – provenance, deterministic computation, verification – applies anywhere trust in AI output matters: chemicals, legal, healthcare. Models are commoditizing fast. The trust layer is what's missing, and the market is massive.
You'll define the research agenda that makes AI trustworthy for enterprise decisions. At Kepler, we've solved hallucination not by making models smarter, but by architecting systems where hallucination is structurally impossible. AI interprets intent. Code retrieves data. A semantic layer connects them. Every output traces to its source.
Now we need someone to push the frontier of what's possible within this architecture.
You'll lead research across agentic systems, memory architectures, retrieval mechanisms, and evaluation frameworks. You'll have access to completely differentiated financial data: structured filings, earnings transcripts, market feeds, research reports, live audio, all normalized with full provenance. This isn't another research lab building demos. Your work ships to production, powering research workflows where financial professionals make million-dollar decisions.
This role is for researchers who want to build AI systems that actually work in high-stakes environments, where every answer must be defensible and every insight must trace back to truth.
Posted June 10, 2026