Jobs at Productboard

Lead the AI architecture for Productboard Insights, designing agent-native systems, context infrastructure, self-healing workflows, evaluation processes, and AI code-review optimization. Own technical direction across Insights, Spark, and platform teams; drive features from concept through beta; optimize quality, latency, cost, and reliability; write production code; and develop engineers through documentation and reviews.

Own the architecture enabling AI-first product engineering, including agent-native standards, context infrastructure, self-healing incident workflows, autonomous agent features, and AI code review optimization. Ship full-stack product features, set technical direction, and partner with product and design teams. The role requires strong backend engineering, production LLM experience, distributed systems expertise, and practical use of AI coding tools.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship end-to-end product features, set technical direction, evaluate LLM reliability and trade-offs, and accelerate validated learning through close collaboration with engineering, product, and design teams.

Lead architecture and end-to-end development of AI-first product features. Establish agent-native engineering standards, context infrastructure, self-healing incident workflows, multi-step agent systems, and AI code-review optimization. Collaborate with engineers, product managers, and designers to ship validated product improvements quickly while setting technical direction. The role requires strong backend and full-stack development, distributed systems expertise, production LLM experience, and effective use of AI coding tools.

Lead architectural work for AI-native backend engineering, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step LLM agents, and AI code-review optimization. Ship backend code and AI-first product features while setting technical direction and collaborating with product and design teams. Apply strong distributed-systems, event-driven architecture, and production LLM expertise to accelerate validated learning.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship production software, establish technical direction, and partner with product and design teams to accelerate validated learning. The role requires strong backend and distributed-systems expertise, hands-on LLM product experience, and practical use of AI coding tools.

Own the architectural layer for AI-native product engineering, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step LLM agent systems, and AI code review optimization. Ship backend code, set technical direction, and partner with product teams to deliver validated AI-first features. The role requires strong backend, distributed systems, event-driven architecture, LLM, and AI coding tool experience.

Own the core product experience for roadmapping, prioritization, and customer engagement. Set strategy, roadmap, metrics, and AI-first workflows; determine how Spark integrates with structured product interfaces; lead cross-team initiatives; partner with engineering on platform foundations; prototype using AI coding tools; engage customers; and mentor PMs. The role requires deep enterprise B2B SaaS, agentic product, systems thinking, research, information architecture, and technical product leadership experience.

Own the enterprise AI stack for Productboard Spark, including permissions, audit, usage limits, model routing, BYOM, automation, and governance. Define strategy and roadmap, translate enterprise customer needs into scalable product capabilities, lead cross-functional initiatives, establish success metrics, prototype with AI coding tools, and support enterprise rollouts. The role requires deep experience shipping agentic AI products, enterprise B2B SaaS, governance controls, and large-scale customer deployments.

Own the strategy, roadmap, quality, and end-to-end customer experience for Productboard Spark’s AI Insights and agentic product capabilities. This includes designing agent behavior, data and retrieval pipelines, evaluation frameworks, interactive outputs, and product metrics. The role requires hands-on prototyping, analysis of model traces and evals, customer research, cross-functional leadership, and mentoring PMs while partnering closely with the founders.

Own Productboard’s product-wide UX and interaction architecture for its AI-first product. Define patterns for agentic workflows, structured UI, generated artifacts, navigation, and object models; establish design standards and language; raise usability and coherence across teams; lead critiques and mentor designers; and personally design complex foundational experiences. Prototype in code using AI tools, validate with users, and partner with Product, Engineering, and executives to guide product direction.

Manage a portfolio of B2B SaaS accounts through activation, adoption, and renewal support. Guide product and engineering leaders in using Productboard Spark for AI-first product management, configure AI context, coach prompt usage, deliver activation sprints, monitor adoption metrics, and identify expansion signals. Partner with account executives and renewal managers, document customer insights, improve playbooks, and provide product feedback. The role requires customer-facing technical advisory skills, product management knowledge, and professional AI experience.

Plans, executes, and optimizes SEO, AEO/GEO, PPC, paid media, CRO, and AI-powered marketing workflows. Analyzes campaign performance, manages budgets and vendors, creates ad and campaign copy, documents processes, and reports insights. Collaborates with demand generation, analytics, content, design, web engineering, and product marketing teams to drive acquisition, retention, and expansion for Productboard Spark.

Build AI-native data engineering agents, multi-agent workflows, semantic layers, evaluation frameworks, monitoring, and reliable data pipelines. Develop dbt and Snowflake models supporting revenue, marketing, and product analytics while improving data quality, testing, documentation, and deployment practices. Collaborate with Analytics, BizOps, Product, and Engineering teams to deliver self-service AI-enabled analytics.

Plan, build, and optimize SEO, AEO/GEO, PPC, paid media, CRO, and AI-powered marketing workflows. Analyze performance, manage budgets and vendors, create campaign and ad copy, report insights, document processes, and coordinate digital projects with marketing, analytics, design, and web engineering stakeholders. The role requires hands-on execution in B2B technology marketing and ownership of acquisition, retention, and expansion outcomes.

Own and optimize the marketing and revenue technology stack, including marketing automation, CRM integrations, data schemas, lead processing, campaign operations, segmentation, and attribution. Build scalable GTM workflows and integrations, support AI-enabled marketing processes, and execute campaigns involving audience segmentation, asset management, email builds, A/B testing, and deployment. Collaborate cross-functionally with marketing and revenue stakeholders.

Build and lead full-stack growth features across onboarding, monetization, retention, and AI-driven experimentation. Own architectural direction for AI-first product engineering, including agent workflows, scalable codebases, API contracts, documentation, and evaluation systems. Partner with product, design, and data teams to validate ideas quickly, measure outcomes, mentor engineers, and improve learning velocity.

Lead Developer Experience engineering and AI-native architecture initiatives. Build Kotlin services, Go internal tooling, developer platforms, CI/CD improvements, observability, reliability guardrails, and self-service workflows. Establish AI-ready codebase standards, context infrastructure, agent workflows, and AI code-review optimization. Partner across teams, define and measure developer-experience metrics, validate solutions with users, and promote product-minded engineering practices.

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

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.