Staff Backend Engineer, Agentic AI

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.

As a Staff Backend Engineer, you'll design the backbone of our intelligent finance systems: scalable services, resilient APIs, and thoughtful data models. Our backend is primarily Python on GCP, and your work will ensure we can move quickly while maintaining enterprise-grade reliability and security.

- 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.

- Experience with distributed systems, event-driven architectures, or microservices.

- Familiarity with GCP services (Pub/Sub, Cloud Run, Cloud SQL, BigQuery, KMS, etc.).

- Passion for building the backend that powers intelligent products.