Product engineer (AI-native)

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Engineering
Product engineer (AI-native)
Berlin or remote (Europe) Full-timeReports Directly to CPTO
This is a senior engineering role with full product ownership. You will work directly with the founders, understand what users need, decide what to build, and ship it to production. The difference: you do this at 5-10x the throughput of a traditional engineer because you have rebuilt your workflow around AI agents, agentic coding tools, and LLM-assisted development. The practices you establish will define how the entire engineering team works as qlaris grows.
What you would be building
We publish this because the right candidate evaluates the science, not just the pitch.
qlaris is a synthetic consumer research platform: surveys, AI focus groups, and behavioral UX audits powered by psychometrically calibrated Synthetic Populations.
Tech stack: Next.js 16, React 19, TypeScript (strict), Tailwind CSS v4, Cloud Firestore, Vertex AI Gemini, Anthropic Claude, Google Cloud Tasks, Browserbase + Stagehand for browser automation.
Current product: 12 question types, AI-powered survey generation (3-agent pipeline), real-time streaming results, debate mode (AI focus groups), behavioral audits on live URLs, workspace collaboration, MCP server.
The codebase is modern, well-structured, and already AI-friendly with comprehensive CLAUDE.md files, design tokens, and context layers. You would be productive on day one.
What you will do
End-to-end product engineering
Own features from problem definition to production: talk to users, define scope, design the solution, write the code, ship it, measure it
Make product decisions autonomously - you do not wait for a PM to write a ticket. You understand the user, the business, and the technical constraints, and you act on that understanding
Ship production-quality code daily, not weekly. The speed comes from AI-augmented workflows, not shortcuts on quality
AI-native engineering practices
Use Claude Code, Cursor, and agentic coding workflows as your primary development environment - not as an occasional assist
Design and maintain the context layers (CLAUDE.md, design tokens, architecture docs) that make AI tools effective across the team
Build internal tooling, MCP integrations, and automation that multiplies the team's output
Establish the engineering practices that new hires will learn - your workflow becomes the team standard
Product and user focus
Use qlaris itself to validate product decisions before building - run concept tests, messaging checks, and feature prioritization with Synthetic Populations
Collaborate directly with the founders on product strategy, roadmap prioritization, and GTM
Maintain a short feedback loop between what you build and how users respond - analytics, support signals, and direct conversations
What you bring
Required
Strong software engineering foundation: CS degree or equivalent depth, 4+ years of professional software development experience
Demonstrated product sense: you have shipped features that users loved, not just features that worked. You can explain why you built what you built, not just how.
Proficiency with modern web stack: React, TypeScript, Node.js, Firestore or similar NoSQL. Our stack is Next.js + React 19 + Tailwind.
Daily, productive use of AI coding tools: Claude Code, Cursor, Copilot, or equivalent. You should be able to describe specific workflows where AI agents made you meaningfully faster, not just "I use Copilot for autocomplete."
Ability to work autonomously in an early-stage environment: low process, high ownership, direct founder access
Strong written communication: you document decisions, write clear PRs, and communicate asynchronously
Strong differentiators
Experience building agentic workflows or MCP integrations
Track record of shipping consumer-facing products at startups
Familiarity with LLM APIs (Anthropic, OpenAI, Google Vertex AI) and prompt engineering
Previous experience as a founding or early engineer where you shaped the engineering culture
Background in research tools, data products, or developer tools
Not required
A specific number of years of experience - we care about what you can ship, not when you started
Management experience - this is a hands-on building role
A portfolio of open source contributions - shipping product matters more than side projects
What we offer
Define AI-native engineering from the ground up
You are not adopting someone else's best practices. You are creating them. The workflows, tooling, and development culture you establish will be what every future engineer at qlaris learns.
Full product ownership
No handoffs between PM, design, and engineering. You own the feature from idea to production. This is how the best products get built - by engineers who understand users.
Direct founder access
You work with both founders daily. No layers, no translation loss. Your judgment shapes the product.
Meaningful equity
You are building core product at an early stage. Your compensation reflects that.
Competitive salary
We pay well. Early stage does not mean below market.
Full flexibility
Berlin hybrid or fully remote within Europe. Measured on output, not office hours.
A real AI-native codebase
This is not a legacy codebase where you fight the code to use AI. The codebase was built for AI-assisted development from day one: comprehensive context files, design tokens, structured data, clear architecture.
Why this role is different
Most companies hiring "AI engineers" want someone to build AI features. We want someone who uses AI to build everything faster. The distinction matters: you are not a machine learning engineer. You are a product engineer whose entire workflow is AI-augmented - from understanding the problem to shipping the solution. This role is for someone who has already made that shift and wants to make it the standard.
Apply for this role
No cover letter template needed. Just your details and a link to your work.
Name *
Email *
LinkedIn profile or link to CV *
All roles
Engineering
Product engineer (AI-native)
Berlin or remote (Europe) Full-timeReports Directly to CPTO
This is a senior engineering role with full product ownership. You will work directly with the founders, understand what users need, decide what to build, and ship it to production. The difference: you do this at 5-10x the throughput of a traditional engineer because you have rebuilt your workflow around AI agents, agentic coding tools, and LLM-assisted development. The practices you establish will define how the entire engineering team works as qlaris grows.
What you would be building
We publish this because the right candidate evaluates the science, not just the pitch.
qlaris is a synthetic consumer research platform: surveys, AI focus groups, and behavioral UX audits powered by psychometrically calibrated Synthetic Populations.
Tech stack: Next.js 16, React 19, TypeScript (strict), Tailwind CSS v4, Cloud Firestore, Vertex AI Gemini, Anthropic Claude, Google Cloud Tasks, Browserbase + Stagehand for browser automation.
Current product: 12 question types, AI-powered survey generation (3-agent pipeline), real-time streaming results, debate mode (AI focus groups), behavioral audits on live URLs, workspace collaboration, MCP server.
The codebase is modern, well-structured, and already AI-friendly with comprehensive CLAUDE.md files, design tokens, and context layers. You would be productive on day one.
What you will do
End-to-end product engineering
Own features from problem definition to production: talk to users, define scope, design the solution, write the code, ship it, measure it
Make product decisions autonomously - you do not wait for a PM to write a ticket. You understand the user, the business, and the technical constraints, and you act on that understanding
Ship production-quality code daily, not weekly. The speed comes from AI-augmented workflows, not shortcuts on quality
AI-native engineering practices
Use Claude Code, Cursor, and agentic coding workflows as your primary development environment - not as an occasional assist
Design and maintain the context layers (CLAUDE.md, design tokens, architecture docs) that make AI tools effective across the team
Build internal tooling, MCP integrations, and automation that multiplies the team's output
Establish the engineering practices that new hires will learn - your workflow becomes the team standard
Product and user focus
Use qlaris itself to validate product decisions before building - run concept tests, messaging checks, and feature prioritization with Synthetic Populations
Collaborate directly with the founders on product strategy, roadmap prioritization, and GTM
Maintain a short feedback loop between what you build and how users respond - analytics, support signals, and direct conversations
What you bring
Required
Strong software engineering foundation: CS degree or equivalent depth, 4+ years of professional software development experience
Demonstrated product sense: you have shipped features that users loved, not just features that worked. You can explain w

Work arrangement
Yes