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Formulary Financial is building a platform for managing billions in assets for alternative funds. As a Staff Software Engineer, you'll own whole verticals end-to-end alongside the CTO and principal engineer, working across the full stack from data model to API to UI to deployment.
Key areas you'll impact:
**Real-time Calculation Engine**: Build systems for allocations, NAV, and capital account computations that must tie out to the cent. Every calculation is precise and auditable.
**Lineage & Traceability**: Implement systems where every number traces back to its source in client files, with derived numbers showing their formulas. Users can walk the full calculation chain from screen result back to origin.
**Workflow Orchestration**: Design durable workflows that coordinate LLM agents, deterministic validators, and human sign-offs, where each component owns the steps it's best suited for.
**Money Movement**: Own production bank connections, payment rails, and reconciliation workflows. Capital calls settle against real bank activity and tie out automatically.
**Automated Audit & Data Quality**: Build a continuous data-quality layer that checks for misattributed ledger entries, broken tie-outs, and drifted balances.
**Semantic Modeling**: Work with an ontology metamodel that generates the platform's types, authorization rules, and graph schema. Changes propagate across the entire platform.
**Authorization Systems**: Implement permissions as live queries over the fund hierarchy, where access rights fall out of how funds, GPs, and LPs actually relate.
**Client Surfaces**: Contribute to web, mobile, and agentic Excel experiences used by fund administrators, GPs, and LPs daily.
You'll need 12+ years as a true generalist engineer with deep TypeScript across the stack (React frontend, GraphQL/gRPC backend). Experience building systems where money must tie out—payments, billing, ledgers—is essential. You're comfortable with complex data structures: versioned history, deep hierarchies, reconciliation. You understand distributed systems in practice: idempotency, retries, durable execution. You build with AI daily and have shipped LLMs to production with real guardrails. You architect on AWS and Kubernetes, own data decisions, and maintain strong testing, CI/CD, and security practices. You've raised the bar on teams before.
