Staff Software EngineerCNTR

This is a Staff Software Engineer role owning the platform layer that every engineering pod depends on: agent frameworks context systems orchestration verification observability data infrastructure and developer tooling. This is a high-ownership deeply technical role where you identify the bottlenecks limiting the company work directly with engineers accountants and customers and build the abstractions that make hard problems simple. Staff here means depth of impact not span of control everyone keeps writing code. The person in this seat will multiply the output of the entire engineering team from the ground up in-person in New York City.

Details

• Location: New York City NY In-person
• Employment: Full-time
• Experience: 6 years
• Schedule: Startup hours 6 days a week 9am to 7pm or 8pm IMPORTANT!
• Visa & work authorization: None available

About the company
A seed-stage company building the AI operating system for the CFO office deploying audit-ready AI agents that connect to a companys existing finance stack including ERPs banks billing payroll CRM contracts spreadsheets email and Slack to automate the work behind close revenue recognition reporting cash management and audit readiness. The goal is for finance teams to review exceptions while AI handles the rest powered by a unified finance context layer that captures transactions policies contracts historical decisions and accounting judgment. The company has raised $9M from a well-regarded investor group that includes prominent AI and fintech leaders.

What youll own

• Own the agent harness the entire company builds on defining abstractions for context verification guardrails observability and developer tooling so every pod can ship audit-grade AI agents on shared rails
• Design and build the financial context graph including ledger state dimensions policies contracts precedent and entitlements keeping it coherent scalable and multi-tenant safe
• Define the verification auditability evals and observability standards that determine whether AI output is safe enough for a customers books and enforce them by construction
• Architect the financial data platform owning ingestion normalization reconciliation and the canonical ledger model that turns data from ERPs banks billing payroll CRMs and email into a trustworthy source of truth
• Design the durable execution layer for long-running AI workflows and the exactly-once audit-ready path that safely writes back to ERPs and systems of record
• Build abstractions and architectural patterns that make every pod faster and more effective raising engineering standards across the company

What were looking for

• 6 years of software engineering experience with deep expertise in distributed systems data infrastructure workflow engines transactional systems integration platforms or AI/agent platforms
• Strong backend proficiency in a modern language such as Python Go Java or Rust (the teams stack is Python; willingness to ramp fast from another language is required)
• Proven track record building platform systems that multiplied the output of other engineers including designing abstractions frameworks or infrastructure that multiple teams depended on
• Experience leading architectural decisions under ambiguity: identifying foundational problems making technical bets with incomplete information and seeing the long-term consequences of those decisions
• Startup experience at an early stage (pre-seed through Series B) where ownership is high resources are constrained and there is no separation between strategy and execution
• Comfort shipping full-stack when the work calls for it including TypeScript on the frontend side
• Able to work in-person at the New York City office on the stated schedule (6 days a week 9am-7/8pm)

Nice to have

• Staff or Principal IC experience at a high-growth startup with systems that multiple teams depended on
• Background in accounting fintech ERP payments treasury audit or compliance software or experience building ledger and sub-ledger systems
• Designed and operated AI agent platforms in production including context systems evals verification guardrails observability and orchestration
• Built systems where correctness mattered more than speed or operated under SOX SOC 1/2 financial audit or other regulatory requirements
• Built platform infrastructure developer tooling or architectural primitives that became the foundation other engineers built on