Platform Engineer

Introducing Kepler 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 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. Platform Engineer What You'll Own You'll own the infrastructure foundation of Kepler's AI research platform. The cloud, databases, deployment fabric, and security posture that financial institutions stake their workflows on. Every model invocation, every analyst query, every enterprise deployment runs on what you build. This is the first dedicated platform hire. You're defining how Kepler ships, scales, and earns trust from the most security-conscious buyers in the world. This isn't a service role. It's the role that defines how Kepler ships. As part of your role, you will: Own the AWS account architecture, Pulumi baseline, and deployment pipelines the whole company depends on. Stand up the enterprise deployment model (single-tenant, in-VPC, BYOK). Set the security baseline (SOC 2 controls, audit logging, identity, network isolation) that converts procurement reviews into closed deals. Establish the paved path: service templates, CI/CD, secrets management, and observability defaults so every engineer ships safely without thinking about it. In the longer term you will also: Drive Kepler's database strategy at scale: Postgres performance, replication, tenant isolation, schema evolution as we ingest billions of provenance-tagged records. Own production reliability: SLOs, on-call rotations, incident response, postmortems. Build systems that earn trust under real load. Set the architectural patterns that compound as the team grows: capacity planning, cost discipline, and service standards that others reach for. How We Work We're a close team, working together in an office in New York. We use AI tools heavily: Cursor, Claude Code, whatever makes us faster. Fluency is assumed. Our users are analysts at firms where a wrong number costs real money. The feedback loop on what you ship is hours, not quarters. The pace is startup-fast but the engineering bar is high. We care about getting things right, not just getting things out. If you've worked somewhere that moves fast but ships broken software, this is different. If you've worked somewhere that's rigorous but slow, this is also different. The team has strong backgrounds and low ego. We expect everyone to roll up their sleeves and handle the unglamorous problems: the weird regressions, the subtle bugs, the last minute debugging session before a demo. We move as a team, not as a collection of individuals. Who You Are You think of platform as a product, not a service. Your users are other engineers, and you measure success by whether they ship faster because of you. Friction is a bug. You'd rather make every path to prod 30 seconds shorter than ship a new service yourself. You've been the one paged at 3am, and you decided to fix the underlying system, not just the symptom. You set the patterns that compound: architectural standards, cost discipline, observability defaults. The choices that look small today become the team's foundation a year from now. From the technical side: 5+ years building and operating production cloud platforms. You've owned the AWS account, the IaC repo, and the pager. No upper limit, comp scales with experience. Deep AWS fluency (VPCs, IAM, KMS, EKS or ECS, RDS) and IaC discipline. Pulumi preferred, Terraform or CDK experience translates. Track record building developer platforms: CI/CD, paved-path tooling, internal services that other engineers actually want to use. Shipped enterprise auth (SSO/SAML/SCIM) and the audit/SOC 2 work that comes with it. Comfortable in Rust or Python for systems work. You don't need to be the team's strongest Rust dev, but you can read and contribute to it. From the personal side: You care what the analyst does with what you shipped, not whether the code was clever. You'd rather fix something than file a ticket about it. You'll tell someone their design has a flaw before the PR goes in, not after. You communicate before it's a problem, and when a teammate needs something from you, they don't have to ask twice. You know what it feels like when the plan changes twice in a day and the work still has to ship. Don't check every box? Apply anyway. We prioritize problem-solving ability, systems thinking, and drive to build the infrastructure layer that makes trustworthy AI possible. Our Technical Stack Backend: 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, Pulumi. You'll define the rest. AI: Model-agnostic by design. We currently use Claude and GPT. The model is the replaceable part. Mentorship & Growth Direct mentorship from engineers who built Palantir's core systems: Weekly 1:1s with senior engineers who've architected enterprise-scale distributed systems. Deep architectural reviews and guidance on system design. Clear growth path toward technical leadership and system ownership. Learn by building production systems that power real financial research. Working at Kepler 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 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.

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