Platform Engineer

About this role

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,…