Founding Engineer (Calculation Engine, Rust)

You will be our first teammate on the calculation engine, and you will design and ship an auditable, graph-based computation engine for financial models—including target-seeking / back-solving into authorable formulas—while remaining transparent and traceable.

Requirements

Experience designing calculation engines

Numerical Methods & Solvers

3+ yrs implementing non-linear optimizers, constraint solvers, or Monte-Carlo engines (e.g., IPOPT, CPLEX, interior-point, Levenberg-Marquardt).
Demonstrated back-solving features (Goal Seek, Solver-like) in production software.

Dependency-Graph Architecture

Built dynamic DAGs for recalculation (spreadsheet, build-system, or reactive programming engines).
Know incremental recompute, cycle detection, memoization.

Data-Structure & Perf Engineering

Fluency in cache-efficient data models (columnar, compressed sparse).
Proven SIMD, GPU, or multi-thread scaling.

Auditability by Design

Designed immutable calc ledgers or event-sourced systems with provenance metadata at every node (inputs, intermediate states, version IDs).
- Regulators & auditors will ask "show me exactly how <this number> arrived at $-1.7 M" and we need to be able to walk them through the system

Bonus points

AI Optimism

You have optimism towards async AI fixing bugs and making tweaks to the codebase and you plan for that by checking your interfaces and design with an LLM to see where your approach is weak.

We have high optimism for async AI handling the majority of the coding in the long-tail of our software process. But, we don't find AI effective at every stage of development.

Generally, we think about AI usage following a trend like the following;

AI Optimism

You have optimism towards async AI fixing bugs and making tweaks to the codebase and you plan for that by checking your interfaces and design with an LLM to see where your approach is weak.

We have high optimism for async AI handling the majority of the coding in the long-tail of our software process. But, we don't find AI effective at every stage of development.

Generally, we think about AI usage following a trend like the following;

AI Optimism

You have optimism towards async AI fixing bugs and making tweaks to the codebase and you plan for that by checking your interfaces and design with an LLM to see where your approach is weak.

We have high optimism for async AI handling the majority of the coding in the long-tail of our software process. But, we don't find AI effective at every stage of development.

Generally, we think about AI usage following a trend like the following;