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Staff Backend DevX Engineer (Prague/Brno/Remote EU) in Remote Europe, Praha, Hlavní město at Productboard

NewEmployment Type: Full-Time
Productboard
Remote Europe, Praha, Hlavní město, Czech Republic
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Job Description

The opportunity
The way software is built is changing, and fast! Engineers who embrace AI-native workflows today will define the next decade of product development. Those who don’t? They will be explaining what they used to do.
At Productboard, this transformation to being AI native is not a side project; it is our entire focus. AI first is our operating model, and this is how we work. We are building an AI-first Product Engineering organization where learning velocity matters as much as shipping. Our northstar: turn ideas into validated learning twice as fast. More experiments. Tighter feedback loops. Better outcomes.
We are looking for a Staff Backend DevX Engineer who wants to be at the center of this transformation, not watching it happen from the outside. The Developer Experience team’s mission is to boost engineering velocity and developer happiness by creating simple, powerful tooling and paved paths. We treat DevX like a product: we listen to feedback, measure outcomes, and focus on the highest-impact work so teams can ship faster with less friction and more confidence. Join us, and help shape the future of AI-powered product development.
This role can be based in Prague or Brno, where we have offices (hybrid), or remotely within Europe.
How we work
We've moved away from process and toward judgment. The shift looks like this:
  • One named driver per initiative. End-to-end ownership of the outcome, the path, the decisions, and the learnings. No approval chains. The driver decides; leadership unblocks.
  • One-page pre-reads as the unit of decision. Big work starts with a brief covering the problem, expected outcome, risks, and effort. Leadership reads it and decides. No 30-slide decks. No two-week alignment cycles.
  • Continuous delivery, no quarterly planning. Roadmap committed one month out. Beyond that, AI moves too fast for longer cycles to mean anything. We ship to internal first, then beta, then GA. Validation comes from real usage.
  • PMs and designers ship to production. Not just specs and Figma. They prototype with AI tools and ship alongside engineers. PMs own what gets built and when. Engineers own how.
If you've been a founding engineer or founder, this should feel familiar. If you've been waiting for a place that operates like that at scale, this is one.
The system around you
We invest before we expect. We are actively looking for the next set of constraints to remove before they slow us down.
  • Best AI tools from day one. Cursor, Claude Code, Codex, Glean. No waiting list, no approval process. If a better tool shows up tomorrow, you get that one too.
  • AI Champions embedded on every team. Engineers (not coordinators) who pair with you, unblock you, and help you move faster with agents. Four hours every week dedicated to team enablement.
  • A codebase built for agents. Curated AGENTS.md files, repo-versioned skills, clean contracts. Continuously evaluated, not accumulating.
  • Ship It with AI days. Two days every six weeks. No meetings for ICs. Pick a real problem, try a new AI workflow, ship to production within 48 hours.
  • Knowledge that compounds. Monthly engineer-to-engineer events where we share what we're experimenting with, learning, and shipping with AI.
What you will do
  • Build and evolve Kotlin services and frameworks that streamline the inner loop (APIs, build/test tooling, automation, paved paths).
  • Maintain and improve our internal developer tool written in Golang.
  • Accelerate CI/CD: improve caching/parallelism, increase test reliability, and shorten feedback cycles.
  • Partner across teams to standardise workflows, enable self-serve integrations, and reduce cross-team friction.
  • Instrument and improve: define DX metrics (lead time, build time, flakiness), run experiments, and iterate based on data.
  • Improve reliability and safety with sensible defaults—observability, guardrails, and secure-by-default patterns.
  • Make our codebase AI-ready: define clear module boundaries, improve API contracts, add semantic context, and build the structured documentation that makes AI agents more effective across every repo.
  • Design and implement agent workflows that go beyond chat: multi-step reasoning, tool use, autonomous task execution, and human-gated checkpoints.
  • Run experiments, validate with real users, and iterate based on evidence. We measure learning velocity, not just output.
  • Collaborate closely with product managers and designers to shape what we build, not just how we build it. We expect a product mindset, not just technical execution.
  • Act as a knowledge multiplier, sharing what you learn across and beyond your team to raise the bar for everyone.
Who we're looking for
  • Have 5+ years of professional engineering experience
  • Experience with Kotlin/Java in production (JVM performance, testing, dependency management).
  • Solid grasp of cloud-native engineering (containers, Kubernetes; AWS/IaC a plus).
  • Pragmatic approach to developer platforms, internal tooling, and CI/CD at scale.
  • Nice to have: exposure to Go and interest in multi-language ecosystems.
  • Previous experience with working on developer or internal tooling.
  • Contributing to open source.
  • Strong communication skills and collaborative mindset.
  • Curiosity, adaptability, and a proactive, startup-friendly attitude.
  • Bring product thinking to engineering work. You can articulate why something matters for users, not just how it works technically.
  • Embrace AI as a daily tool in your own workflow. You use AI coding assistants, iterate on prompts, and constantly look for ways to move faster.
  • Are curious about what agent native architecture looks like: how to structure codebases, APIs, and documentation so AI agents can operate effectively at scale.
Our tech stack
  • AI layer: Python, Pydantic AI, Braintrust
  • Frontend: TypeScript, React, Relay, GraphQL
  • Backend: Kotlin, Ruby (legacy services we're modernizing), with new services built in Kotlin
  • Storage: PostgreSQL, MongoDB, Elastic, Redis
  • Data pipeline: Python, Keboola, Looker, Snowflake
  • Infrastructure: AWS, Cloudflare, Kubernetes, Terraform

Job Location

Remote Europe, Praha, Hlavní město, Czech Republic

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