Senior Staff AI Engineer

Senior Staff AI Engineer - Product Engineering at Productboard

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What’s new An agent built for product teams It’s specialized for your product work, remembers all your context, and is built for your whole team. Learn more

Use Cases Customer Feedback Analysis Centralize feedback and surface opportunities & insights with AI Product Roadmapping Align stakeholders on product plans with interactive roadmaps Customer Engagement Validate ideas & planned features, and share what's been launched Product Specification Draft product specs with AI that knows your product & customers Strategic Product Planning Ensure every team's product priorities support your objectives

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Senior Staff AI Engineer Prague, Czechia Apply now Department Product Engineering Employment type Full Time Workplace type Hybrid Share this job

• Location: Prague, Czechia (hybrid). Brno or remote within the EU is possible. Relocation support is available.

Only the best engineers are being built here. At Productboard, we are defining AI-first Product Engineering. Not as a one-time achievement, but as a baseline we raise every time we reach it. We remove every bottleneck as it surfaces and rebuild how product teams work from first principles, not just adding AI to existing workflows. AI tools are everywhere now. Agents can write code. That’s today’s baseline.

What we're building is the environment where the baseline makes you better: focused on outcomes, writing specs that agents build from, leveling up with every feature you ship. We are defining what AI-native product development becomes. We're all in.

What you will build

You'd lead the architectural work that makes our codebase AI-native. Specifically:

• Agent-native architecture standards. Clear API contracts, semantic naming, well-defined module boundaries. The patterns that keep AI effective as our systems grow.
• A context infrastructure layer. Repo-versioned guidance that AI tools load automatically — improving the output of Cursor, Claude Code, and Codex simultaneously.
• Self-healing workflows for incidents. Agents that triage alerts, pull logs, surface relevant history, suggest remediation. Humans intervene where judgment matters.
• Agent workflows that go beyond chat. Multi-step reasoning, tool use, autonomous task execution with human-gated checkpoints — for product features, not internal demos.
• Systematic optimization of AI code review. Earlier detection of correctness, security, and maintainability issues, with the noise filtered out before it reaches a human reviewer.

The problem Product managers (PMs) get more customer feedback than they can read. Today AI processes only part of it. Insights can arrive weeks after the customer spoke, and PMs still link each need to strategy by hand.

We want AI to process all customer feedback and tell each PM what customers need, why it matters, and how it connects to strategy.

When it works, a PM opens Productboard and sees: "'Slow dashboard loading' emerged across 23 customers (USD 1.4M ARR). Maps to Objective 'Improve Performance'. Competitor X shipped performance improvements last week." The PM accepts the insight and asks Spark, our AI agent for product teams, to draft a spec.

You will own the AI architecture that produces that insight, and set the AI patterns that other domains at Productboard follow.

The outcomes we measure Feedback informs decisions automatically. Themes map to objectives, initiatives, and the product hierarchy. PMs accept, reject, or refine them. PMs prioritise by business impact. Each theme shows annual recurring revenue (ARR), open deals at risk, and links to customer churn.

Competitor moves show up next to feedback. When a competitor ships something, PMs see which feedback themes it touches. Insights makes Spark smarter. Spark's answers are only as good as Insights data.

• Targets: PMs accept more than 80% of AI insights, more than 90% of feedback connects to a feature, objective, or initiative, and nobody processes feedback by hand. We have not reached these numbers yet. Reaching them is the job.

What makes this hard

• Changes travel. Insights data feeds Spark, so a change in how we group feedback changes what Spark tells PMs. You weigh those effects across domains before a change ships.
• Messy ground truth. Often there is no single right answer to "which theme does this note belong to?" Two good PMs may disagree. You decide how we measure quality honestly.
• Cost. Every AI call has a price, and customers pay for AI usage. As usage grows, you decide where a smaller model, caching, or skipping work keeps quality and protects margin.
• Influence without authority. Most teams you guide do not report to you, and some are new to building with AI. You lead through clear arguments and clear documents.

What you will do

• Own the Insights AI architecture and its technical direction: what context each step gets, which tools agents may call, and how we know an output is good.
• Set the quality bar for AI in Insights, and make evals the gate every change passes before it ships.
• Lead AI architecture decisions that span Insights, Spark, and the platform.
• Bring teams with different priorities to one written decision, such as the agent conventions every domain follows.
• Drive the largest Insights initiatives as the named driver.
• Take a new AI feature to customer beta: set the milestones and acceptance criteria, agree scope with the PM, and decide what ships.
• Write production code on the hardest parts, every week.
• Grow the engineers around you. Your docs and reviews let any engineer pick up an AI workflow and change it safely, without waiting for you.

How we work Productboard runs AI-first product engineering. Coding agents write much of the code; engineers own design, review, and results. Each initiative has one named driver and starts from a one-page pre-read. We ship to internal users first, then beta, then everyone. We commit the roadmap about a month ahead, and PMs and designers ship to production too.

• AI layer: Python, Pydantic AI, Vespa, Braintrust, Temporal, LaunchDarkly, Datadog, and Anthropic models. Around it: Kotlin (some legacy Ruby), TypeScript, React, GraphQL, PostgreSQL, MongoDB, Elastic, Redis, Snowflake, AWS, Kubernetes, and Terraform.

What you need

Typically 10+ years building production systems, with strong backend skills You have designed LLM systems that customers use, measured their quality with evals, and made the trade-offs between quality, latency, cost, and reliability.

Hands‑on experience with search, retrieval, clustering, or entity matching over large text collections. Deep experience with distributed systems and long‑running workflows. A record of setting technical direction across several teams or domains, including teams that did not report to you. Clear written English.

You can explain a technical decision on one page, starting from the customer problem. The right to work in the EU.

Nice to have You built an AI platform or shared AI standards that several product teams adopted. Founder or founding engineer experience: you took an AI product from idea to paying customers. Experience with product management, analytics, or customer feedback tools. If you meet most of the must‑have list, please apply.

Our Compensation

• Salary range: 3,185,000–4,775,000 CZK annually

Our Benefits

Include Stock options MacBook + 34″ monitor Work from home stipend to support your home office setup 5 weeks of vacation + 9 sick days Flexible working hours and home office Budget for online courses, books, and conferences 2 weeks of fully paid parental leave Fertility & Family‑Building Support with Carrot Mental Wellness Program with Soulmio to support your well-being and self‑care 1 Volunteer Day per year to support causes close to your heart, plus donation matching from Productboard Free snacks, drinks, and yummy catered lunches from White Circus at the office every day Free MultiSport card On‑site bouldering wall, boxing bag, and workout mats Team events, such as happy hours, off‑sites, and retreats Free year‑round access to Prague Zoo

Relocation Opportunities If joining us means making a move, we're here to help make that transition easier. Candidates must have the legal right to work in the EU. While we are unable to provide visa sponsorship for this role, we're happy to support relocation to Prague for candidates already authorized to work in the EU.

Relocation Support We offer a one‑time relocation bonus ranging from $6,000 to $13,000 USD, depending on your personal situation, whether you're moving on your own or with a partner or family. This bonus is intended to help offset moving expenses and support your transition into your new city.

While it may not cover every cost, it provides meaningful financial support as you get settled. If you're thinking about relocating and want to explore what this could look like for you, we'd be happy to have that conversation.

About Productboard

Productboard is the leading intelligent product management platform, used by 6,000+ companies including Salesforce, SAP, Autodesk, and Kroger. Headquartered in San Francisco, with engineering hubs in Prague and Brno. Backed by Index Ventures, Kleiner Perkins, Sequoia Capital, Bessemer, Tiger Global, and Dragoneer.

We've spent the last decade building the foundation for how product teams work. We're now in our 0‑to‑1 moment again, building Productboard Spark — the AI‑first, agentic experience that turns raw feedback and signals into clear problems, strong specs, and faster execution.

Spark is what powers everything you'd be working on. Are you building with us? If the work above is what you want to spend the next chapter of your career on, apply below!