Staff Software Engineer, Full Stack (Hybrid, NYC)

https://www.formulary.co/careers/engineering/staff-software-engineer-full-stack What Impact You Will Own and Drive As one of a handful of engineers alongside the CTO and principal engineer on a platform managing billions in assets, you'll own whole verticals end-to-end — data model to API to UI to deployment. Real-time calculation engine : Allocations, NAV, and capital accounts computed on the fly. Every number ties out to the cent. Lineage on every figure : Every number traces back to the exact spot in the source file it came from, whatever format the client sent. Derived numbers show their formulas, so you can walk the full calculation chain from a result on screen back to its origin. Orchestrating AI, humans, and determinism: Durable workflows where LLM agents, deterministic validators, and humans sign-off each own the steps they're best at. Money movement : Production bank connections, payment rails, and reconciliation workflows. Capital calls settle against real bank activity and tie out automatically. Automated audit : A data-quality layer that continuously checks the books for misattributed ledger entries, broken tie-outs, and drifted balances. Ontology metamodel : A single semantic model of the fund world generates the platform's types, authorization rules, and graph schema. Change the model once and the whole platform follows. Authorization as a graph : Permissions are live queries over the fund hierarchy. Who can see what falls out of how funds, GPs, and LPs actually relate. Client-facing surfaces : The web, mobile, and agentic Excel experiences that fund administrators, GPs, and LPs use every day. What You Need 12+ years of hands-on engineering as a true generalist. Deep TypeScript across the stack. We're React on the front, GraphQL and gRPC services behind it. You've built systems where money has to tie out: payments, billing, ledgers. Fund admin experience optional, precision required. You're comfortable in complex data: versioned history, deep hierarchies, reconciliation. Our data is hard because of its structure, not its size. You know distributed systems in practice: idempotency, retries, durable execution. Your workflows are safe to run twice. You build with AI daily and have shipped LLMs to production behind real guardrails. You architect on AWS and Kubernetes and own the data decisions under it. Strong testing, CI/CD, and security habits. You've raised the bar on a team before.