Founding engineer

Founding Engineer - Core Intelligence

We're solving a very hard, high-value problem: modeling large, fast-moving real-world data sets - and turning that model into decisions that create measurable economic value.

We're an early-stage startup, and this role owns the core intelligence layer end-to-end: the data, the models, and the engine that turns messy signals into action. It's the hardest problem in the company, and it's yours to define.

What you'd build

• A pipeline that turns large volumes of messy, unstructured public data into one structured, living model of a complex system.

• A scoring and ranking engine that evaluates and prioritizes entities within that system.

• A causal and simulation layer: measuring what actually drives outcomes, and rehearsing decisions before they're made in the real world.

Background we're looking for

• Strong data/ML engineering - you build and ship production pipelines and models, not just notebook prototypes.

• Real depth in at least one of: causal inference / experimentation, simulation / modeling, graph systems, or ranking/scoring.

• Comfortable with messy real-world data: scraping, entity resolution, missing and noisy signals.

• Founding-engineer mindset: builds v1 fast and scrappily, owns it end to end, thrives in ambiguity, ships pragmatic over perfect.

• LLM/agent tooling; and you've taken something from zero to production.

Who this is for

• Someone who wants to define the core technology of a company, not maintain someone else's - excited that the hardest, highest-value problem is theirs to own, with meaningful equity and real ownership of the core IP from day one.

• It is possible to start on a part-time position basis

Education

Quantitative/technical background - CS, ML, Statistics, Applied math, or Econometrics. An advanced degree is a plus; strong builders from quant, competitive ML, or founding-engineer paths are just as welcome.