[All remote jobs](https://jobicy.com/jobs.md)Open role[![Ruby Labs logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/0799a7b7-221-1.jpeg)](https://jobicy.com/company/ruby-labs.md)Remote opportunity at[Ruby Labs](https://jobicy.com/company/ruby-labs.md) # Technical AI Product Manager Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/ruby-labs.md)Share24 Aug 2026Published36Listing views3Application actions24 Sep 2026Apply before Opportunity details ## About this role. AI SummaryRuby Labs is seeking a Technical AI Product Manager to own and expand the connector ecosystem supporting its direct-to-consumer AI products. The role centers on prioritizing, launching, monitoring, and improving MCP servers, third-party APIs, and application integrations based on user demand, commercial impact, and engineering effort. This person will work closely with AI engineers on LLM features, prompt systems, structured outputs, agentic workflows, and evaluation pipelines. They will also use analytics and observability tools such as Mixpanel and Langfuse to measure adoption, reliability, latency, cost, retention, and engagement. The position is remote for candidates located roughly within four hours of Central European Time and operates in a fast-paced, high-accountability environment. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior, technically demanding product role requiring independent end-to-end ownership of an AI integration platform and sound judgment across product, engineering, analytics, and commercial trade-offs. The fast-iteration environment and responsibility for reliability, experimentation, and cross-functional alignment increase the role's complexity. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$180,000US market range$145k–$215k0$237k AI insightNo salary range was provided. Based on the U.S. market, a Technical AI Product Manager with 4+ years of product experience, AI/LLM fluency, API-platform expertise, and ownership of an integration ecosystem would commonly command approximately $145,000 to $215,000 annually in base salary, with an estimated median of $180,000 USD. Actual compensation may vary because this role is remote, CET-aligned, and structured under an independent contractor agreement. ## Core skills Skills and capabilities most closely associated with this opportunity. [Technical Product Management](https://jobicy.com/jobs?search_keywords=Technical%20Product%20Management.md)[AI Product Management](https://jobicy.com/jobs?search_keywords=AI%20Product%20Management.md)[LLM Applications](https://jobicy.com/jobs?search_keywords=LLM%20Applications.md)[Model Context Protocol (MCP)](https://jobicy.com/jobs?search_keywords=Model%20Context%20Protocol%20MCP.md)[API Integrations](https://jobicy.com/jobs?search_keywords=API%20Integrations.md)[Connector Ecosystems](https://jobicy.com/jobs?search_keywords=Connector%20Ecosystems.md)[Agentic Workflows](https://jobicy.com/jobs?search_keywords=Agentic%20Workflows.md)[Product Analytics](https://jobicy.com/jobs?search_keywords=Product%20Analytics.md)[Mixpanel](https://jobicy.com/jobs?search_keywords=Mixpanel.md)[Langfuse](https://jobicy.com/jobs?search_keywords=Langfuse.md) Sample interview questionsHow would you prioritize which connectors or MCP integrations to build first?I would use a transparent scoring framework that combines validated user demand, expected revenue or retention impact, strategic fit, implementation effort, ongoing maintenance risk, reliability requirements, and time to value. I would validate assumptions through customer research, support data, search or request volume, and product telemetry, then review the ranked roadmap regularly as usage and engineering constraints change. What metrics would you use to evaluate the health of an AI connector ecosystem? I would track connector adoption, active users per connector, successful tool-call rate, error rate, latency, authorization completion, task completion, retention impact, support volume, and cost per successful outcome. For high-value integrations, I would also monitor downstream business metrics such as conversion, engagement, and incremental revenue, segmented by user cohort and connector version. How do you translate an LLM or agentic workflow into buildable product requirements? I begin with the user job, target workflow, expected outcome, and failure boundaries. I then specify inputs, tools and permissions, structured-output schemas, prompt or orchestration requirements, acceptance criteria, fallback behavior, evaluation datasets, observability events, and measurable launch thresholds so engineering and QA can implement and validate the feature consistently. Describe how you would make a build-versus-buy decision for an integration platform component. I would assess strategic differentiation, speed to market, total cost of ownership, security and compliance needs, vendor reliability, extensibility, integration complexity, and lock-in risk. I would favor buying or adopting established infrastructure for commodity capabilities when it accelerates learning, while building internally when the capability is core to product differentiation, control, or long-term economics. How would you run an experiment for a new AI integration feature? I would define a clear hypothesis, target audience, primary success metric, guardrail metrics, and decision criteria before launch. After instrumenting the feature and validating event quality, I would use an appropriate A/B test or phased rollout, analyze results by meaningful cohorts, review qualitative feedback and reliability data, and make a documented ship, iterate, or sunset decision. ## About us Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we’re always looking for passionate individuals to join us. Learn more about our story at: [https://rubylabs.com/about-us/](https://rubylabs.com/about-us/) ## About the role At Ruby Labs, we are building Direct-to-Consumer products in the AI category. We’re looking for a Technical AI Product Manager to own and scale the integrations and connector ecosystem that powers our AI features — the third-party APIs, app integrations, and MCP (Model Context Protocol) servers that let our products reach beyond the model itself. This is a high-ownership, technical role. Your core mandate is to scale connectors — growing the number, quality, and reliability of integrations available to our users — while making data-driven decisions about what to build, in what order, and how to measure success. You’ll operate inside an AI engineering squad, working shoulder-to-shoulder with engineers on prompt systems, structured outputs, agentic workflows, and evaluation, and collaborating closely with product, growth, data, and billing teams. We’re looking for someone technical enough to read API docs and talk to engineers without a translator, who treats AI tools as a core part of their daily workflow, and who measures success in outcomes — not 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. * Prioritise 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 LLM features, agentic workflows, and tool use, into clear, buildable 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 prioritise 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 (Claude and others) as a core part of the daily workflow for prototyping, specification writing, analysis, and problem-solving. ## Qualifications * 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 (Claude or similar) for real work — this is how the team operates; it is not optional. * 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 (OpenRouter or similar) and LLM observability / evaluation tooling (Langfuse, LangSmith). * Familiarity with the TypeScript / Node.js / Next.js ecosystem. * Experience in a startup or fast-paced, high-iteration product environment. ## Location Ruby Labs operates within the CET (Central European Time) zone. Applicants from any country are welcome to apply for the position as long as they are located within approximately ± 4 hours of CET. This ensures optimal collaboration and communication during working hours. ## Benefits Discover the perks of being part of our vibrant team! We offer: * Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance. * Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days. * Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge. * Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them. * Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here: [https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0#heading=h.ndsdl4wapxtt](https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0#heading=h.ndsdl4wapxtt) Be part of our fast-growing team and seize this excellent opportunity for personal and professional growth! ## Interview Process After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps: * Recruiter Screening (40 minutes) * Technical Interview (60 minutes) * Second Interview (45 minutes) * Final Interview (30 minutes) ## Life at Ruby Labs At Ruby Labs, we move fast, aim high, and expect the same from our team. We’re not here to play small—we’re here to build, grow, and win. That means we look for people who are ambitious, driven, and ready to give their best every single day. This is a place for individuals who thrive under pressure, embrace challenges, and see opportunity in every obstacle. If you’re hungry to achieve, motivated by impact, and want to grow at the speed of your own ambition, Ruby Labs offers the platform to make it happen. Here, effort is matched with reward. We recognize those who go all in and deliver results, and we create space for people who want more—more responsibility, more growth, and more success. #LI-Remote Show more [Apply now >](https://jobicy.com/jobs/151445-technical-ai-product-manager-3.md) > Annual salary information is not provided for this position. 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