Founding Engineer

Pling

Pling is the employer — EngRadar is a job radar, not a recruiter. We track this posting from their own careers page and send you straight there; we never handle applications or CVs.

Germany - Remote Full-time Posted 2mo ago

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About Pling

Pling builds the operational backbone for medical practices, starting in dentistry. Most dental software handles the patient journey (documentation, treatment plans, billing) — we handle the operations enabling it: equipment management, regulatory compliance, team coordination, service procurement — a segment the wider industry has largely overlooked. Our vision: expand horizontally into other medical specialties and vertically into a marketplace connecting practices with manufacturers, technicians, and service providers.

Six-digit ARR, 90%+ gross margins, growing ~4x year over year
Proven product-market fit with a diverse customer base — from single practitioners to enterprise-level dental chains
Bootstrapped by design — profitable, growing without external capital, raising our first round in Q4/Q1
Sticky by design — compliance obligations make Pling mission-critical infrastructure; once integrated, customers don’t churn

The team

The founding team combines deep startup, product, and domain expertise.

Paolo (CEO) — Stanford-educated, spent 8 years in Silicon Valley where he built and sold a startup, ex-BCG, two prior exits
Henrik (CPO) — ex-BCG, N26, ImmoScout24, early-stage startups
Jonas (CMO) — former clinician at Uniklinik Düsseldorf, ran his own practice, self-taught engineer. Currently owns Pling’s technical architecture and codes alongside the team.

The role

You’ll own Pling’s technical domain — architecture and code, strategy and execution. The role is hands-on and impact-driven: you contribute to product discussions alongside the founders, then own the full technical lifecycle — architecture, implementation, deployment, monitoring, and continuous improvement in production — with a focus on real-world reliability and business impact.

Your mandate is to architect and ship the platform that takes Pling to the next 10x of customers and locations — AI-native by design, built to scale across multi-site groups across Europe — and to build the development pipeline that makes our shipping velocity hard to match.

We believe a small, AI-augmented engineering team can outship teams five times its size. You’ll prove that with us.

What you’ll do

Build complete features end-to-end — data model to API to UI
Own the technical direction — architecture, infrastructure, security, performance
Design and own the LLM pipelines that power Pling’s AI features — prompt design, structured outputs, evals, monitoring, cost tracking
Set engineering standards across code and AI features — testing, CI/CD, observability, code review on one side; eval harnesses, regression checks, and quality metrics tied to business outcomes on the other
Define and lead the migration to our next-generation architecture — the stack choices are yours to shape
Build the development pipeline that makes our shipping velocity hard to match
Build the team’s agentic-AI practice — the shared conventions, review patterns, and guardrails that let a small AI-augmented team ship reliably and scale without drowning in AI slop
Hire and lead — bring on the next engineer when the time is right, and define how the team works

About you

Experience

6—9+ years building production software in fast-paced environments, with at least 2 years focused on LLM-powered features in production
Pragmatic about architecture and tradeoffs; you’ve learned that good engineering usually means removing complexity, not adding it
Comfort across the stack — backend center of gravity, but able to ship end-to-end
Production experience with Python, TypeScript/Node.js, and modern databases (SQL and NoSQL)

AI-native fluency

Deep practical understanding of LLM behavior: prompting, structured outputs, context window management, common failure modes, handling non-determinism, and writing evals that catch regressions before users do
Pragmatic about model selection — pick across providers (Claude, GPT, Gemini, open-weights) based on cost, latency, and quality tradeoffs for each use case
Know when not to reach for an LLM. A lot of good engineering is knowing when a regex, a lookup table, or deterministic code is the right answer. You hate AI slop as much as you love AI leverage.
Proven at team scale, not just solo — you’ve established how a team works with agentic AI: shared conventions, code review calibrated to AI-generated logic, and eval expectations that keep output reliable as the team grows

Builder mindset

You’d rather build than manage. AI excites you because it lets you ship at a scale you couldn’t before.
High agency: you operate independently in fast-changing conditions and don’t wait for permission to solve the problem in front of you.
You love writing and shipping code — and you’re not precious about it. When the problem shifts, you rewrite rather than defend.

Engineering excellence

Code quality is a habit, not an afterthought: testing, review, quality gates
Strong on operational excellence: security, reliability, performance, observability
Confident with CI/CD and production deployments

Collaboration

Clear communicator who explains technical trade-offs to founders, customers, and future hires
Balance speed, quality, and long-term sustainability in your decisions
Fluent English. German is a big plus — our customers operate in German

Why Pling

Strong traction in an attractive niche. Profitable, growing fast, in a category few others are addressing.
An ambitious platform vision. Horizontal expansion to other medical specialties and a vertical marketplace dimension both within reach.
Equity anchored at pre-fundraise valuation. Because we haven’t raised yet, the upside on equity granted now is materially higher than it will be even six months from now.
Dynamic, fast-paced environment with experienced founder-operators and domain experts — direct access, fast decisions.

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