<p>Retire Capital builds a digital retirement withdrawal management platform - a professional cockpit for sustainable asset decumulation. We solve a critical gap in the German financial services landscape: helping financially literate individuals (55+) confidently and sustainably withdraw from their portfolios, optimizing across withdrawal strategies, portfolio allocations, rebalancing disciplines, and historical market environments.</p><p><br></p><p>Our simulation engine evaluates hundreds of thousands of parameter combinations across decades of market data — annuity vs. constant withdrawal rules, corridor-based smoothing thresholds, glide-path rebalancing — to surface the strategies that actually survive real-world conditions. This is applied financial mathematics, not another robo-advisor.</p><p><br></p><p><strong>The Role</strong></p><p><br></p><p><span>You will be the second technical hire and will shape both the product and the engineering culture from the ground up. This is a very senior engineering role: you write code, you ship features end-to-end, and you support setting the technical direction for a platform that will operate under regulatory oversight and handle sensitive financial data.</span></p><p><br></p><p><span>You will engage directly with the founders and work as part of a deliberately small, high-leverage team. Our ideal, short-term target for our engineering team size is 3-5 people — we believe AI-augmented, end-to-end enabled and supported, professional engineers will replace the need for large, specialized teams.</span></p><p><br></p><p><strong>What You Will Do</strong></p><p><br></p><ul><li>Ship end-to-end: Design, implement, test, and deploy features across the full stack — from domain modeling and financial algorithms through API design to interactive UI. No handoffs, no silos.</li><li>Own the architecture: Evolve our architecture, enforce dependency rules, and make pragmatic decisions.</li><li>Advance the platform: Extend a Rust-native fullstack system that compiles a single codebase to both a native server binary and a WASM client — with compile-time guarantees enforcing the boundary between them.</li><li>Build for correctness: Work in a domain where withdrawal math must be numerically stable, validation must be provable and security primitives must be implemented without shortcuts.</li><li>Scale the team: Establish engineering practices that sustain a small, high-output team.</li></ul><p><br></p><p><strong>General Requirements</strong></p><p><br></p><ul><li>7+ years of professional software engineering experience, with meaningful time spent in complex, statically-typed systems. We value depth over breadth — if youve spent years reasoning about ownership, lifetimes, type systems, or protocol correctness, we want to work with you.</li><li>Demonstrated ability to deliver features end-to-end — from data model through business logic to user-facing interface. You dont identify as "backend" or "frontend"; you identify as an engineer who ships.</li><li>Strong foundation in software architecture — you understand hexagonal/ports-and-adapters patterns, dependency inversion, and when to break the rules pragmatically. You know the difference between accidental and essential complexity and you fight the former relentlessly.</li><li>Production experience with regulated or security-sensitive systems — financial services, healthcare, identity, or similar domains where correctness isnt optional and observability matters.</li></ul><p><br></p><p><strong>Technical Background</strong></p><p><br></p><p>We specifically value experience in technologies that require deep, sustained thinking — the kind that cant be shortcut by prompting an LLM. Pre-AI depth in any of the following is a strong signal:</p><p><br></p><ul><li>Rust (strongly preferred): Ownership model, trait-based polymorphism, async runtimes, procedural macros, WASM compilation targets. Our entire production stack is Rust.</li><li>Systems-level programming: Experience with memory models, concurrency primitives, zero-copy abstractions, or performance-critical numerical computation.</li><li>Cryptography & security engineering: Practical experience with authentication protocols, encryption, key management, or secrets handling in production.</li><li>Applied mathematics / quantitative finance: Comfort with annuity formulas, Monte Carlo simulation, portfolio theory, or statistical modeling. Our core engine runs combinatorial parameter grids across decades of market data with strict numerical stability requirements.</li><li>Infrastructure as code: Terraform/OpenTofu on GCP (Cloud Run, Cloud SQL, VPC networking, IAP). Youve operated what youve built.</li></ul><p><br></p><p><strong>AI-First Engineering</strong></p><p><br></p><p>We embrace AI to increase our velocity and to unlock new capabilities.</p><p><br></p><p>This is not a bullet point on a job ad — its a core engineering value. We use Claude Code as a daily co-engineer. We expect you to be fluent in AI-assisted development: prompting, reviewing, iterating, and integrating AI-generated code into production systems.</p><p><br></p><p>But we draw a hard line on accountability:</p><p><br></p><p>While we might outsource some — or sometimes even a lot — of the responsibility of a delivery to AI, we still stay accountable for the results. That means you need to make a reasonable effort to understand, shape, guide, and rework what AI delivers in order to truthfully represent its outputs as a correct solution you are standing by.</p><p><br></p><p>AI is a capability multiplier, not a responsibility diffuser. When you merge code that Claude helped write, your name is on it. You understood why an annuity formula caches its denominator in thread-local state. You verified the corridor threshold logic handles one-way constraints correctly. You checked that an OPAQUE registration flow doesnt leak timing information.</p><p><br></p><p><strong>What This Means in Practice</strong></p><p><br></p><ul><li>You have working experience with AI coding tools (Claude Code, Cursor, Copilot, or similar) and a developed intuition for when AI output needs scrutiny vs. when it can be trusted.</li><li>You can decompose complex tasks into AI-friendly units, provide effective context, and critically evaluate the results — including recognizing subtle correctness issues that pass surface-level review.</li><li>You understand that AI changes what work looks like but not who is accountable for the results.</li></ul><p><br></p><p><strong>Team Composition Philosophy</strong></p><p><br></p><p>We believe the age of AI fundamentally changes how startup engineering teams should be composed:</p><p><br></p><ul><li>End-to-end only: Since AI is a capability enabler, we hire exclusively for engineers who can deliver features across the full stack. No pure-frontend, no pure-backend, no pure-infra roles.</li><li>Small by design: End-to-end capability combined with AI leverage means fewer people shipping more. Our ceiling is 3-5 technical hires — enough to staff on-call rotations with coverage for vacation and illness.</li><li>Depth as signal: We focus on complex, non-mainstream technology (Rust) as a natural filter. 3+ years of pre-AI experience in a demanding technology stack demonstrates the willingness and ability to think and work deeply — a quality that no credential or take-home test can reliably signal in the age of AI.</li><li>References as primary qualifier: We will request and interview named references from your previous positions. In a world where CVs, cover letters, and take-home tests can be AI-generated, references from real colleagues who worked alongside you are the remaining reliable formal signal.</li></ul><p><br></p><p><strong>What We Offer</strong></p><p><br></p><ul><li>The opportunity to shape a regulated fintech product and engineering culture from the earliest stage.</li><li>A Rust-native, architecturally disciplined codebase — not a legacy migration, not a prototype that "will be rewritten later."</li><li>A technical co-founder who writes code, understands the architecture, and will review your PRs with the same rigor you bring to his.</li><li>A working environment that treats AI as a first-class engineering tool, not a novelty or a threat.</li></ul><p><br></p>