Senior software engineer

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.

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.

The Role

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.

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.

What You Will Do

• 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.
• Own the architecture: Evolve our architecture, enforce dependency rules, and make pragmatic decisions.
• 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.
• 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.
• Scale the team: Establish engineering practices that sustain a small, high-output team.

General Requirements

• 7+ years of professional software engineering experience, with meaningful time spent in complex, statically-typed systems. We value depth over breadth — if you've spent years reasoning about ownership, lifetimes, type systems, or protocol correctness, we want to work with you.
• Demonstrated ability to deliver features end-to-end — from data model through business logic to user-facing interface. You don't identify as "backend" or "frontend"; you identify as an engineer who ships.
• 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.
• Production experience with regulated or security-sensitive systems — financial services, healthcare, identity, or similar domains where correctness isn't optional and observability matters.

Technical Background

We specifically value experience in technologies that require deep, sustained thinking — the kind that can't be shortcut by prompting an LLM. Pre-AI depth in any of the following is a strong signal:

• Rust (strongly preferred): Ownership model, trait-based polymorphism, async runtimes, procedural macros, WASM compilation targets. Our entire production stack is Rust.
• Systems-level programming: Experience with memory models, concurrency primitives, zero-copy abstractions, or performance-critical numerical computation.
• Cryptography & security engineering: Practical experience with authentication protocols, encryption, key management, or secrets handling in production.
• 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.
• Infrastructure as code: Terraform/OpenTofu on GCP (Cloud Run, Cloud SQL, VPC networking, IAP). You've operated what you've built.

AI-First Engineering

We embrace AI to increase our velocity and to unlock new capabilities.

This is not a bullet point on a job ad — it's 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.

But we draw a hard line on accountability:

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.

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 doesn't leak timing information.

What This Means in Practice

• 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.
• 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.
• You understand that AI changes what work looks like but not who is accountable for the results.

Team Composition Philosophy

We believe the age of AI fundamentally changes how startup engineering teams should be composed:

• 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.
• 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.
• 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.
• 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.

What We Offer

• The opportunity to shape a regulated fintech product and engineering culture from the earliest stage.
• A Rust-native, architecturally disciplined codebase — not a legacy migration, not a prototype that "will be rewritten later."
• A technical co-founder who writes code, understands the architecture, and will review your PRs with the same rigor you bring to his.
• A working environment that treats AI as a first-class engineering tool, not a novelty or a threat.