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 / Senior AI Product Engineer who wants to be at the center of this transformation, not watching it happen from the outside. 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

Building AI-powered product features.
Enhancing and sustaining our internal tech stack, while identifying and incorporating new state-of-the-art technologies.
Discovering and experimenting across different domains, creating MVPs and POCs, engaging in discussions about findings with fellow engineers and the product team, and planning the execution.
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.

About you

7+ years of professional software engineering experience, with a proven track record of shipping scalable production systems.
Deep hands-on experience with LLMs in real products — including prompt design, context management, evaluation, and understanding real-world limitations (hallucinations, latency, cost, reliability).
Experience building agentic systems is highly valued. Alternatively, deep hands-on work with advanced LLM workflows (tool use, multi-step reasoning, memory, orchestration) and a clear understanding of their trade-offs — along with the drive and technical maturity to quickly evolve toward agentic architectures.
You think like a builder - ideally a former founding engineer or founder - with a strong instinct for shipping what truly moves the product and the company forward.
You’re drawn to agent-native architecture and believe this is where software is heading. You’re excited to rethink codebases, APIs, and documentation so AI agents can reliably operate and scale.
You rely on AI tools daily (e.g., Cursor, Claude Code) and treat them as a core part of your engineering workflow — constantly experimenting, refining prompts, and pushing them to meaningfully increase your speed and output.
Comfortable working in distributed systems and event-driven architectures (e.g., queues, async processing, service-to-service communication).
Able to lead complex technical initiatives end-to-end, raising the bar for architecture, quality, and engineering culture.

About the Team:

The AI team at Productboard is a cross-functional product team, composed of backend, frontend, and machine learning engineers, along with PMs and designers. You might have already explored the AI capabilities within our product, and we aim to build more Copilot features into Insights and other domains, enabling bulk-processing, pre-processing, smart suggestions, and other functionality. In the end, our customers would be presented with aggregated insights & clustered data, which is connected to the features & ideas they plan to build.

To create even more value for customers, we are on a mission to help product teams transform their product management practices, leveraging the power of AI to make impactful product decisions faster through a deeper understanding of different types of data within the Productboard platform (customer feedback, competitors, market intelligence, business strategy, etc.).

Our Tech Stack

AI Layer: Python, Pydantic AI, Braintrust
Frontend: TypeScript, React, Relay, GraphQL
Backend: Kotlin, Ruby, Kafka
Storage: PostgreSQL, MongoDB, Elastic, Redis
Data Pipeline: Python, Keboola, Looker, Snowflake
Infrastructure: AWS, Cloudflare, Kubernetes, Terraform
Business tools: Slack, Jira, Google suite, Zoom, Notion
Full Tech Stack

Our Benefits Include

💰Stock options

💻 MacBook + 34″ monitor

📚 Budget for online courses, books, and conferences

🏝️ 5 weeks of vacation + 9 sick days

🫶 Volunteer Days for you to help causes close to your heart

🥕 Carrot Fertility Benefits

🥗 Free snacks, drinks, and yummy catered lunches

🏋️‍♂️ MultiSport card to access sports facilities

⏰ Flexible working hours and home office

🧑‍🧑‍🧒‍🧒 Parental benefits

🗣️ Language lessons

🍀 Mental Wellness Program to support your well-being and self-care

Staff / Senior AI Product Engineer

Benefits

Job Description