As a Senior Backend Product Engineer, you will develop backend systems, enhance user experiences, collaborate with cross-functional teams, and ensure AI integration in product development.
Only the best engineers are being built here.
At Productboard, we're building the place where AI-first engineering gets defined. Removing every bottleneck as it surfaces, we are fundamentally reimagining what's possible when AI becomes a first-class partner, not a tool. This is where product engineers learn to operate at their highest level in the AI era.
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.
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.
This role is hybrid in Prague or Brno, or remote within the EU.
How we workWe'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 youWe 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.
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
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
