Machine Learning 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 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.

THE ROLE

WHAT YOU'LL OWN

You'll own the models inside Kepler's AI research platform: which model runs each task, when a fine-tuned model beats a frontier one, and the training, evaluation, and extraction systems that make every workflow powerful. Model-agnostic by design doesn't mean the model doesn't matter. It means model choice is a permanent engineering problem, and it's yours. The models you choose and tune sit inside a product financial professionals rely on for million-dollar decisions.

This role is for engineers who want to build foundational technology at the intersection of AI and finance, where your code directly impacts how clients make critical business decisions.

In the first few weeks you might:

- Fine-tune a small model on a high-volume extraction task (footnote tables in 10-Ks, IR decks) and show it beats the frontier model we use today on accuracy, cost, and latency.

- Build an eval harness that scores agent research runs end to end (does every number trace, does every citation resolve) and wire it into CI so regressions get caught before analysts see them.

- Redesign model routing across a workflow: a frontier model where the reasoning is hard, cheaper or fine-tuned models for high-volume extraction and verification steps, with evals proving nothing got worse.

- Take a workflow that succeeds 80% of the time and systematically find the other 20%: better tools, tighter verification rules, different context, a fine-tune, or a different model entirely.

In the longer term, you’ll be given ownership of whole functional areas, from extending our platform to a new industry to leading new architecture as our infrastructure scales.

You'll consistently own systems end-to-end. In a small team, there's nobody to hand things off to.

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've shipped production systems and you care about whether they're correct - not just whether they work on the happy path. You think about failure modes before someone asks you to.

You're comfortable in a codebase you didn't write, moving between a fine-tuning run and the orchestrator code that serves the result in the same day. You're drawn to early-stage not for the title but because you want your work visible in the product, not abstracted behind three layers of management.

From the technical side:

- 5+ years building production software. No upper limit, comp scales with experience.

- You've shipped…