Agentic Systems Engineer Introducing KeplerThe ProblemHigh-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 IsKepler 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 TeamThe founding team spent a combined 40+ years at Palantir building the type of large-scale data infrastructure that Kepler requires. Our CTO created Palantir's first AI platform and built the analytics engine behind $100M+ contracts. Our founding engineers led Foundry's core systems - Ontology, Fusion, Workshop, FoundryML - 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.The RoleWhat You'll OwnYou'll build the agentic infrastructure that powers Kepler's AI research platform. You'll work on the foundational systems that make autonomous AI agents reliable at scale: distributed execution frameworks that run thousands of agents in parallel, evaluation systems that ensure agent quality, context management that maximizes agent performance, and the ontology and provenance systems that let us trace every number back to its source.This role is for engineers who want to work at the frontier of AI systems, building the infrastructure that makes agents trustworthy for enterprise-critical decisions.Within your first 90 days, you will:Ship your first production agent system with senior mentorshipBuild and deploy infrastructure that powers real financial research workflowsSee your code enable agents to conduct research at top financial institutionsTake ownership of a core agentic system from architecture to productionWhat You'll DoBuild agent execution infrastructure: Distributed systems that orchestrate and run massive numbers of agents in parallel with reliability, retry logic, and graceful degradation.Build evaluation systems: Frameworks that measure agent quality, catch regressions, and ensure agents perform reliably across diverse research tasks.Optimize agent performance: Context compression, prompt optimization, model routing, and latency reduction. Make agents faster and smarter.Build ontology and provenance systems: The semantic layer that maps concepts to precise definitions and traces every output back to authoritative sources. This is what makes our platform trustworthy.Integrate AI into production: Language models powering intelligent research workflows with robust error handling, fallback mechanisms, and cost optimization.Own systems end-to-end: Design to production. Services, database optimization, deployment, monitoring.Ship with production excellence: Comprehensive testing, monitoring, deployment pipelines. You own reliability for what you build.What We're Looking For7+ years of software engineering experience shipping production systems at scaleBackend: Python or Node.js, distributed systems, PostgreSQL, Redis, AWSArchitecture: Experience designing systems that scale and handle complex workflowsAI/ML systems: Experience building with LLMs, agent frameworks, or ML infrastructureData: Large datasets, ETL pipelines, knowledge graphs or semantic systems a plusPractices: Git workflows, CI/CD, automated testing, observabilityStrong communicator who can discuss technical trade-offs clearlyCurious about the frontier of AI agents and eager to push what's possibleThrives in fast-paced environments with high ownershipFinancial services experience preferred but not requiredDon't check every box? Apply anyway. We prioritize problem-solving ability, systems thinking, and drive to build transformative agentic infrastructure.Our Technical StackBackend: Rust - agent orchestration, data extraction, computation pipelines.Frontend: TypeScript, React - the analyst workspace and verification interfaces.Data: PostgreSQL, plus direct integrations with official data sources.Infra: AWS.AI: Model-agnostic by design. We currently use Claude and GPT. The model