Staff AI Product Engineer

About Productboard

Productboard was founded in 2014 by Hubert Palan and Daniel Hejl with a simple mission: to help companies make products that matter. In the years since, we’ve grown from a handful of people to hundreds, backed by a number of the most renowned VC firms in Silicon Valley. Today, Productboard is recognized as one of the hottest tech startups, appearing on the Forbes’ Next Billion-Dollar Startups list and being named the most valuable startup in the Czech Republic by Forbes Magazine.

About The Role We're hiring a Staff Engineer to own the architectural layer that makes this real, working alongside our engineers, PMs, and designers shipping AI-first product features end-to-end. You'd ship code, set technical direction, and shape decisions about what's worth building. Your north star here will be: get from idea to validated learning twice as fast. The role is open to hire B2B Contractor or FTE.

What You’ll Do 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’ll Bring You ship with AI every day. You think in outcomes, not output. You can name a tradeoff you made between scope and quality and tell us why you made it. You can describe what you've stopped doing because AI made it unnecessary. You've owned features end-to-end and know what it costs to get something to GA. Concretely, we're looking for: •6-10+ years of production software engineering experience •Strong backend skills in Python, Kotlin, or Java, with experience evolving service-level logic and infrastructure •Hands-on LLM experience in real products: prompt design, context management, evaluation, real understanding of trade-offs (hallucinations, latency, cost, reliability) •Comfort with distributed systems and event-driven architectures (queues, async processing, service-to-service communication) •Daily use of AI coding tools as a core part of your workflow — pushing them, refining prompts, knowing where they break Strong fit: Former founding engineer or founder. A 0-to-1 engineer who turns business insights into prototypes to validate ideas quickly. Someone who's built agentic systems in production, or done deep work with multi-step LLM workflows (tool use, memory, orchestration). 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

*This job posting exists to fill a vacancy.