The Problem

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.

What Kepler Is

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.

The Team

The founding team spent a combined 40+ years at Palantir building the type of large-scale data infrastructure that Kepler requires. Our founding engineers led Foundry's core systems - Ontology, Fusion, Workshop, FoundryML, created Palantir's first AI platform and scaled data products at Meta to 1B+ users.

We’ve paired this deep technical foundation with a repeat founder profile. Our CEO built and scaled a data company to $15M ARR before successfully selling it. He then became Citadel's first Head of Business Engineering, experiencing first hand the problems we are now solving. We have a team who’ve been on both sides: building systems like this at massive scale and selling it into the buyers who need it most.

We’re backed by investors who built the modern AI and data stacks, plus the builders of iconic commercial businesses. This includes founders of OpenAI, Meta AI Research, MotherDuck, dbt Labs and Square as well as PebbleBed, Company Ventures and Mantis VC firms.

What You'll Own

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.

Key Responsibilities

• Define the research agenda: Identify the highest-leverage research problems at the intersection of AI reliability, agentic systems, and enterprise trust. Shape what Kepler builds next.

• Pioneer agentic architectures: Design systems for agent orchestration, task planning, and multi-step reasoning that maintain accuracy and provenance across complex research workflows.

• Advance memory and retrieval: Build novel approaches to context management, knowledge representation, and retrieval that let agents reason over millions of documents while staying grounded in sources.

• Build evaluation frameworks: Define how we measure agent quality, reliability, and trustworthiness. Create benchmarks that matter for real-world financial research.

• Ship research to production: This isn't theoretical. Your work deploys to systems used by top financial institutions. Research validates through real-world impact.

• Build and lead the research team: Hire exceptional researchers. Create an environment where breakthrough work happens and ships.

• Publish and represent Kepler: Contribute to the broader AI community through publications, open-source work, and presence at top venues like NeurIPS, ICML, and ICLR.

• Collaborate with leadership: Work directly with leadership on technical strategy, product direction, and company vision.

What We're Looking For

• PhD in Computer Science, AI/ML, or related field

• 5+ years in AI/ML research with focus on LLMs, agents, retrieval systems, or memory architectures

• Publication record: Strong track record at top venues (NeurIPS, ICML, ICLR, ACL)

• Production experience: You've shipped research to real systems with real users. You understand the gap between paper and production.

• Technical depth: Expertise in transformer architectures, attention mechanisms, retrieval-augmented generation, or memory-augmented systems

• Leadership: Experience building and leading research teams in high-stakes environments

• Research taste: Ability to identify problems that are both technically interesting and practically important

• Strong communicator who can translate complex research into clear implications for product and business

• Intellectually humble. Confident enough to lead in your domain, wise enough to collaborate across disciplines.

• Energized by hard problems where the answer isn't known

Why This Role is Different

• Differentiated data: Access to structured financial data with full provenance that doesn't exist anywhere else. Train and evaluate on real, high-quality data.

• Architecture that matters: We've built the infrastructure that makes AI trustworthy. Your research extends what's possible within a system designed for accuracy.

• Research that ships: Your work powers decisions at top financial institutions. Impact is measured in production, not citations.

• Greenfield opportunity: Define the research agenda for a company building the ground truth platform for AI.

• Founder-level collaboration: Work directly with a founding team that built Palantir Foundry and data infrastructure at Citadel.

Research Benefits

• Open publication policy: We actively encourage publishing at top venues and contributing to the broader AI community. Your research belongs to the field.

• Dedicated GPU capacity: Pre-reserved compute for research experiments. The computational resources to pursue ambitious ideas without fighting for allocation.

• Conference budget: NeurIPS, ICML, ICLR, and any venue where you need to be. Travel, registration, and time to engage with the research community.

• Academic partnerships: Funding and support for collaborations with universities and research institutions.

• Differentiated data access: Train and evaluate on structured financial data with full provenance that doesn't exist anywhere else.

• Research autonomy: Define your own agenda within our mission. We hired you for your judgment.

Our Benefits

• 100% covered top-of-the-line medical, dental, and vision insurance for employees and their families. HSA maxed by the company to the IRS limit.

• Automatic coverage for life, AD&D, and disability insurance.

• Daily lunch in office.

• Unlimited PTO policy.

• Development environment budget - latest MacBook Pro, multiple monitors, ergonomic setup, and any development tools you need.

• "Build anything" budget - dedicated funding for whatever tools, libraries, datasets, or infrastructure you need to solve technical challenges, no questions asked.

• Learning budget - attend any conference, course, or program that makes you better at what we're building.

Our Operating Principles

• Trust as the Default: People do their best work when confidence is mutual. We show our work, keep our promises, and flag risks before they bite. Trust isn't an aspiration - it's the baseline.

• Forward-Deployed with Product DNA: We own customer outcomes while building a product company. We don't win if they don't win.

• Extreme Ownership: If you notice a problem, you own it by making sure it doesn’t fall through the cracks. Authority comes from initiative, not job titles. Once you step up, you're accountable for the outcome.

• Production-First Engineering: We design for critical workloads from day one. Durable execution, blue/green deploys, automated rollbacks, continuous delivery with end-to-end observability.

• Communicate with Intent: Great work disappears without great communication. We push information to the people who need it, when they need it. Silence is never the safe choice.

• Earn it Every Day: Your work speaks for itself. We create an environment where the best idea wins, the strongest work gets recognized, and everyone is held to the same high standard.

• Keep Raising the Bar: Great teams compound. Every hire raises the bar, every win gets named, every person gets the tools and runway to grow.

Kepler is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. We are committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment.