Staff Backend DevX Engineer

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. Why this matters for your career The gap between engineers who are building with AI daily and those who are still watching is widening fast. At Productboard, you won't just use AI tools. You will shape the architecture that makes AI agents effective, define the workflows that make them reliable, and build the evaluation systems that make them trustworthy. You will work in an environment where AI first is not a future aspiration. It is the current standard. The skills you build here , agent native design, multi-model orchestration, eval-driven development, AI-ready codebase architecture, are the skills that will define senior engineering for the next decade. AI is how we build This is not a tagline. It is our daily operating model across Engineering, Product, and Design. PMs and designers don't just spec features here. They prototype with AI tools and ship to production alongside engineers. AI is embedded across the full lifecycle: technical discoveries, spec writing, design exploration, implementation, test generation, code review, CI/CD, and incident resolution. Human judgment stays in control. People own product intent and quality decisions, with strong review practices and guardrails that keep shipping safe. What we are building underneath: Agent native architecture standards: clear API contracts, semantic naming, and well-defined module boundaries that keep AI effective as systems grow A context infrastructure layer with repo versioned guidance that AI tools automatically load, improving the output of Cursor, Claude Code, and Codex simultaneously AI agent workflows for on-call and incident resolution: triage alerts, pull logs, surface relevant history, and suggest remediation Systematic optimization of AI code review to catch correctness, security, and maintainability issues earlier 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-ga ... (truncated, view full listing at source)