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Senior Full Stack Engineer

💼 Full-time🗓 2026-07-30

Core

Building a backend-heavy, systems-level platform to automate subscription operations (energy, telco) and secure fair deals for millions of users.

Role type

Senior Full Stack Engineer (Backend-heavy, Systems Architect)

Builds

Scalable workflow orchestration systems, state machines, and abstraction layers for multi-provider subscription management.

Domain

Fintech / Subscription Management / Energy & Telco

Deliverable

production ML models | product features | infrastructure

Required skills

TypeScript, systems architecture, state machine design, complex workflow orchestration, backend engineering, failure handling, retry logic, rollback mechanisms, AI workflow integration, abstraction layer design, full-stack development

Preferred skills

AI tooling experimentation, building complex configurable products, founder/early employee mindset, cross-functional problem ownership

Technologies

TypeScript, AI-friendly tooling

Responsibilities

Architecting agnostic systems for multiple providers and markets, designing robust and configurable workflows, orchestrating human-in-the-loop AI tasks, building complex state machines, shipping end-to-end solutions without PMs.

Seniority

Senior, hands-on IC

Rewrite
## About the Role We're building the platform that secures fair deals on any subscription — energy today, any other subscription type tomorrow. We have PMF and hundreds of thousands of users, but to reach millions, we need to automate operations that are currently manual. You'd be building the platform that makes that possible. This is backend-heavy, systems-level work — the kind where you're holding multiple interacting systems in your head and hunting for the edge cases that will break them. ## The Context: SwitchUp Tech SwitchUp Tech builds the technology for two sister organizations: SwitchUp.de (for profit) and Fairbraucherschutz (not-for-profit). ## The Engineering Challenges - The Abstraction Layer Challenge: How do we architect a system agnostic to the Provider (Vattenfall vs. Deutsche Telekom) and the Market (Energy vs. Telco), so we can scale without rewriting core logic? - The Robustness/Configurability Paradox: Workflows that are highly configurable (to experiment fast and move to new subscription types easily) but robust enough to handle millions of subscriptions. - Human-in-the-Loop AI: Orchestrating workflows where agents start tasks and humans intervene only when confidence drops — with proper failure handling, retries, and rollback. ## The Stack TypeScript-first, AI-friendly tooling designed to scale to hundreds of millions of workflow instances a year. See our full stack and why we chose each tool → ## How We Work - No PMs/POs. No specs. You own a business problem end-to-end — from sitting with operations to build understanding, to architecting the system, to shipping and measuring impact. - AI-forward. As new capabilities emerge, we rethink how we work. Lots of room for experimentation and knowledge-sharing — everyone is both student and teacher. - If you're energized by colleagues who explore new capabilities together the moment they drop, you'll fit in. If that sounds exhausting, we're not the right place. ## Who You Are - 5+ years of engineering experience across the stack. You enjoy learning new languages and tools - A systems thinker who enjoys complexity — orchestration layers, state machines, data models with the right level of abstraction. - Backend > frontend. You can work across the stack, but your energy comes from the systems layer. - Deep experience. You've seen enough failures to know how to avoid them. - A builder who doesn't wait for permission. Likely a founder, early employee, or the person who "went rogue" to get things done. ## Who You Are NOT - Someone who picked an AI coding tool 6 months ago and hasn't questioned it since - A specialist who wants to stay in one corner of the codebase - An engineer who prefers others to define the problem so you can focus on implementation ## Application Requirements No cover letter needed. We want to see how you think. - Your CV / LinkedIn / GitHub - A system you've built that's similar to ours: complex workflows, state machines, highly configurable products. What was the problem, and what made it hard? - What improvements you've recently made to your AI-enabled workflow — what worked well, what's still holding you back?
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