This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Truss runs a custom-built back office that powers our reviews, case work, and risk decisions. It works — but most of it still runs through human review, and the volume is only going up. We're hiring a Back Office Engineer to change that. Your job is to build the automation layer on top of our back office. Most of the reviews and cases that run through it today shouldn't need a human. Your work will: automate the routine reviews end-to-end, build the systems that let us handle cases at the scale we're growing into, and design the architecture that catches fraud before it happens rather than after. This is a high-leverage applied AI role. The systems you build will run at high volume against real operational workloads, so quality, observability, and the right guardrails matter from day one. We want someone who can move fast, build things that hold up under scrutiny, and treat the back office as a serious engineering surface — not a backwater. You'll work directly with the founder. Truss is a small team — 11 people today — so there's no layer between you and the decisions. What you ship matters, and you'll see the impact immediately.
Job Responsibility
Automate back office reviews
Build automation to handle cases at scale
Build the architecture to catch fraud before it happens
Apply AI thoughtfully
Requirements
Minimum 3 years of professional software engineering experience
Strong instincts for automation and workflow systems — queues, state machines, retries, idempotency, audit logging
Hands-on experience shipping AI/LLM-driven features in production, with real opinions about evaluation, guardrails, and when AI is and isn't the right call
A pragmatic, ops-aware working style
Payments or fintech experience is required
Nice to have
Experience building fraud prevention or risk decisioning systems
Background working closely with operations, trust & safety, or compliance teams
Prior experience with evaluation pipelines for LLM-based decisions in regulated environments