Staff Backend Engineer, Agentic AI

About Airwallex

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own.

We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific.

We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles to see it in full.

How you'll make impact

T:0 is building an AI-native accounting platform that helps businesses close the books, reconcile, and report continuously. As a Staff Backend Engineer, you'll design the systems that make intelligent financial workflows reliable, auditable, and ready to scale.

• 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 using FastAPI and Pydantic to power agentic workflows, AI copilots, and real-time financial infrastructure.

• Own data architecture end to end, including schema design, event models, ledger primitives, and storage layers optimized for auditability, traceability, and scale.

• Develop resilient infrastructure primitives—including queues, retries, idempotency, observability, and backpressure handling—that let teams ship quickly without sacrificing reliability.

• Build feedback loops at the intersection of systems and AI, where product data continuously improves models and models improve system behavior.

• Ship platform foundations, including internal frameworks, SDKs, and tooling that accelerate engineers across the organization.

• Partner closely with ML and product teams to productionize research-grade ideas into customer-facing systems.

• Define the architecture of an AI-native financial stack, designing software with AI as a first-class runtime rather than bolting AI onto existing systems.

You'll work from our San Francisco office three days a week.

What we're looking for

Essentials

• 8+ years of backend engineering experience, including substantial experience with Python at scale.

• Deep knowledge of SQL and NoSQL database design.

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

• Familiarity with GCP services such as Pub/Sub, Cloud Run, Cloud SQL, BigQuery, and KMS.

• Strong understanding of security and compliance best practices.

• Experience designing and operating reliable, production-grade backend systems.

• A track record of owning complex technical problems end to end.

• A strong interest in building the backend systems that power intelligent products.

Preferred

• Experience building agentic workflows, AI copilots, or AI-native products.

• Experience with financial systems, ledgers, payments, accounting, or other high-integrity domains.

• Experience building internal platforms, frameworks, SDKs, or developer tooling.

• Experience partnering closely with ML and product teams to productionize research or prototypes.

You'll thrive here if

• You're comfortable owning the roadmap yourself.

• You own the outcome and you don't wait for permission to fix what's broken.

• You're comfortable with ambiguity. Give you a problem, not a prescription, and you'll run with it.

• You hold yourself to a high standard even when no one is watching. Quality and accountability aren't external expectations, they're internal ones.

• You say what you mean. You're direct, but you lead with respect.

• You know when to align and when to just decide. Collaboration isn't a way to avoid accountability.

• You connect dots across functions and hold the big picture without losing grip on the details.

• You ask "why" before jumping to "how."

• You simplify complexity rather than hiding behind it.