Technical AI Product Manager

About Ruby Labs

Ruby Labs is a leading tech company dedicated to creating and operating innovative consumer products across the health, education, and entertainment industries. Our forward-thinking teams are shaping the future of consumer-led products, and we are always seeking passionate individuals to join us. Learn more about our journey at https://rubylabs.com/about-us/.

About the Role

We are building Direct-to-Consumer products within the AI category and are looking for a Technical AI Product Manager. This role will be responsible for owning and scaling the integrations and connector ecosystem that powers our AI features. This includes third-party APIs, app integrations, and Model Context Protocol (MCP) servers that extend our products' capabilities beyond the core model.

This is a high-ownership, technical position. Your primary objective will be to scale connectors—increasing their number, quality, and reliability—while making data-driven decisions about product development priorities and success measurement. You will work within an AI engineering squad, collaborating closely with engineers on prompt systems, structured outputs, agentic workflows, and evaluation. You will also partner with product, growth, data, and billing teams.

We are seeking a candidate who is technically proficient enough to understand API documentation and communicate effectively with engineers, treats AI tools as an integral part of their daily workflow, and measures success by outcomes rather than effort.

Key Responsibilities

• Own and drive the roadmap for scaling the connector ecosystem, including the number, quality, and reliability of integrations (MCP servers, third-party APIs, and app integrations) that power AI features.
• Prioritize integrations and connectors based on user demand, business impact, and engineering effort.
• Write clear product specifications and acceptance criteria for new connectors, working closely with AI engineers.
• Define and own the framework for evaluating, onboarding, monitoring, and maintaining connectors over time.
• Translate AI capabilities, including Large Language Model (LLM) features, agentic workflows, and tool use, into clear, actionable product requirements.
• Partner with the AI engineering team on prompt systems, structured outputs, and evaluation pipelines, ensuring product requirements are reflected in technical design.
• Make pragmatic build-versus-buy decisions and define the scope of integration infrastructure.
• Own features end-to-end, from discovery and specification through QA, launch, and post-launch iteration.
• Define success metrics for connectors and AI features, including adoption, reliability, latency, cost, retention, and engagement impact.
• Design and run experiments and A/B tests, making ship, iterate, or kill decisions based on quantitative results.
• Build and maintain dashboards in Mixpanel and use observability tools such as Langfuse to monitor AI and connector performance and health.
• Surface actionable insights and recommendations to engineering and leadership teams on a regular cadence.
• Own and prioritize the integrations product backlog.
• Collaborate closely with AI engineering, growth, data, and billing teams to deliver initiatives reliably and on time.
• Communicate technical trade-offs, priorities, and roadmap decisions clearly to both technical and non-technical stakeholders.
• Use AI tools (e.g., Claude) as a core part of the daily workflow for prototyping, specification writing, analysis, and problem-solving.

Requirements

• 4+ years in product management, with a strong track record on technical, platform, API, or integration products.
• Demonstrated end-to-end ownership of products—from hypothesis through production and iteration.
• Solid technical fluency: comfortable reading API documentation, understanding data schemas and JSON, and partnering with engineers without needing everything translated.
• Practical understanding of how LLMs work and what modern AI products involve—prompts, structured outputs, agents/tool use, and their limitations.
• Working familiarity with the concepts behind MCP (Model Context Protocol), connectors, or integration/developer-platform products.
• Hands-on, daily use of AI tools (e.g., Claude) for real work.
• Strong analytical skills and hands-on proficiency with a product analytics tool (Mixpanel preferred)—funnels, cohorts, and dashboards.
• Excellent communication and stakeholder-management skills, with comfort in a remote, asynchronous environment.

Nice to Have

• Direct, hands-on experience with MCP—building, integrating, or shipping MCP servers or clients.
• Experience growing an integrations marketplace or connector ecosystem (scaling both breadth and reliability of third-party integrations).
• Experience with AI gateways/model routing (e.g., OpenRouter) and LLM observability/evaluation tooling (e.g., Langfuse, LangSmith).
• Familiarity with the TypeScript/Node.js/Next.js ecosystem.
• Experience in a startup or fast-paced,