Member of Technical Staff - Full Stack, Credit

Stuut is transforming how B2B companies turn revenue into cash. Businesses worldwide have trillions tied up in receivables, yet the work between order and payment still relies on fragmented systems and labor-intensive manual processes. Stuut’s AI agent works across order management, credit, collections, payments, cash application, disputes and deductions, carrying context through every step. Customers reduce DSO by 47%, collect 40% more cash and eliminate 70% of manual work without replacing their ERP.

Stuut is already trusted by finance teams at companies including Honeywell, NCR and ZoomInfo, from Fortune 10 enterprises to scaling mid-market businesses. We are backed by a16z, Khosla Ventures, Activant, 1984 Ventures, Page One and Microsoft.

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  Build and own core systems powering Stuut’s credit and financing products

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  Develop reliable exposure, balance, eligibility, and limit calculations across complex financial data

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  Design deterministic policy and decision systems that make credit outcomes explainable and auditable

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  Model financial state across invoices, payments, credits, disputes, orders, and related events

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  Build systems that safely handle delayed, duplicated, missing, or conflicting upstream data

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  Create controls for reconciliation, idempotency, stale state, overrides, corrections, and failure recovery

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  Integrate external credit data and internal behavioral signals into decision workflows

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  Partner closely with product, data, and operations to translate real customer workflows into scalable product behavior

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  Build reusable credit-domain primitives without introducing customer- or ERP-specific logic into the core system

• Have experience building credit, lending, payments, accounting, or other financial systems where correctness and controls matter

• Have built deterministic policy engines, decision systems, ledgers, balance calculations, or exposure systems

• Understand concepts like reconciliation, idempotency, audit trails, data provenance, and failure recovery

• Are comfortable reasoning through incomplete, delayed, duplicated, or contradictory financial data

• Think explicitly about state transitions, overrides, corrections, exceptions, and the difference between confirmed and pending financial state

• Have strong systems design and product engineering judgment and can trace a financial result back to exactly how it was produced

• Want ownership of a difficult product area and naturally question a number until you understand where it came from

• Enjoy building explicit, deterministic systems that can eventually support many different financial products

• Are highly proficient in Python and have experience working with technologies like Postgres, Snowflake, and AWS; experience with DBOS is a plus

• Thrive in a high-growth startup where engineers work closely with the product and own problems end to end