Staff Backend Engineer - AI-Powered Financial Infrastructure

t0; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Airwallex in San Francisco is seeking a Staff Backend Engineer to design scalable services, APIs, and data models for our AI-enabled financial stack. The role focuses on Python (FastAPI), GCP, reliability and security in an enterprise-grade environment.

You will own end-to-end data architecture, build production-grade systems, partner with ML/product teams, and ship platform foundations that empower engineers across the org.

Qualifications

• 8+ years backend experience with Python at scale.
• Deep knowledge of SQL/NoSQL database design.
• Experience with distributed systems, event-driven architectures, or microservices.
• Familiarity with GCP services (Pub/Sub, Cloud Run, Cloud SQL, BigQuery, KMS).
• Strong grasp of security and compliance best practices.
• Passion for building the backend that powers intelligent products.

Responsibilities

• Design and operate distributed, event-driven systems.
• Build typed, production-grade APIs and services in Python (FastAPI + Pydantic).
• Own data architecture end-to-end—schema design, event models, ledger primitives, storage layers.
• Develop resilient infrastructure primitives (queues, retries, idempotency, observability, backpressure).
• Work at the intersection of systems + AI, building feedback loops where data improves models and models improve system behavior.
• Ship platform foundations, not one-off features—internal frameworks, SDKs, tooling for engineers.
• Partner closely with ML + product teams to productionize ideas into customer-facing systems.
• Help define the architecture of an AI-native financial stack.

Skills

Python

SQL/NoSQL

Distributed systems

GCP

Security

Backend design

Job description

Airwallex in San Francisco is seeking a Staff Backend Engineer to design scalable services, APIs, and data models for our AI-enabled financial stack. The role focuses on Python (FastAPI), GCP, reliability and security in an enterprise-grade environment.

You will own end-to-end data architecture, build production-grade systems, partner with ML/product teams, and ship platform foundations that empower engineers across the org.