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This team designs intelligent, autonomous systems that eliminate financial operations for companies globally. We automate everything from bookkeeping and reconciliation to tax, payroll, forecasting, and document understanding: turning hours of work into seconds. The team blends deep research pedigree with proven industry experience. We operate with high standards, are tool-agnostic, and expect engineers to own problems end-to-end. We collaborate closely, push boundaries responsibly, and ship exceptional work quickly. You’ll solve unprecedented technical challenges using unique, end-to-end financial datasets no one else in the industry has.
Job Responsibility:
Design and operate distributed, event-driven systems that process high-volume, high-sensitivity financial data with strict correctness and latency guarantees
Build typed, production-grade APIs and services in Python (FastAPI + Pydantic) that power agentic workflows, AI copilots, and real-time financial infrastructure
Own data architecture end-to-end schema design, event models, ledger primitives, and storage layers optimized for auditability, traceability, and scale
Develop resilient infrastructure primitives (queues, retries, idempotency, observability, backpressure handling) that let teams ship fast without sacrificing reliability
Work at the intersection of systems + AI, building feedback loops where product data continuously improves models and models autonomously improve system behavior
Ship platform foundations, not one-off features internal frameworks, SDKs, and tooling that accelerate every engineer in the org
Partner closely with ML + product teams to productionize research-grade ideas into real customer-facing systems
Help define the architecture of an AI-native financial stack not bolting AI onto software, but designing software assuming AI is a first-class runtime
Requirements:
5+ years backend experience with Python at scale
Deep knowledge of SQL/NoSQL database design
Experience with distributed systems, event-driven architectures, or microservices