AI Product Manager Mid Level - USA

Cogniify•,

5d•Remote

About The Position

We're hiring a mid-level Product Manager to own and grow AI-driven features and products. You'll work across engineering, design, data science, and business stakeholders to identify problems worth solving with AI, define what "good" looks like, and ship products that actually work in production, not just in a demo. This isn't a research role. You won't be building production models yourself. But we're not looking for someone who just writes PRDs and hands them off. We want someone who has actually gotten their hands dirty: built a prototype, chained some prompts together, used AI to automate a real workflow, or shipped a scrappy internal tool. If your AI experience is limited to writing requirements for a team that built the thing, that's not what we're looking for.

Requirements

3-5 years of product management experience, with at least 1-2 years working on AI/ML-powered products or features
A concrete example of something you built or shipped using AI, not just specced. This could be a side project, an internal tool, a prototype using an LLM API, a workflow you automated, or a feature you personally prototyped before handing off for productionization. Be ready to walk through it in detail: what you built, what tools you used, what broke, what you changed
Working knowledge of how ML/AI systems are built and evaluated: enough to have informed conversations with data scientists and engineers, not enough that you need a CS degree
Strong written and verbal communication; you can explain a confusion matrix to an exec and a business requirement to an engineer
Track record of shipping products and owning outcomes, not just writing requirements for someone else to own
Comfortable with ambiguity. AI product work has a lot of "we don't actually know yet"

Nice To Haves

Retail or e-commerce experience (merchandising, personalization, search/recommendations, inventory, or customer service AI applications)
Experience with LLM-based products specifically (chatbots, copilots, agents, RAG systems)
Familiarity with prompt engineering, evaluation frameworks, or model fine-tuning concepts
Experience working with responsible AI, fairness, or safety review processes

Responsibilities

Own the roadmap for one or more AI-powered product areas from problem definition through launch and iteration
Translate ambiguous business problems into clear product requirements and success metrics
Partner with engineering and data science teams to scope what's feasible, what's expensive, and what's a bad idea
Use AI tools yourself in your day-to-day work (research, drafting specs, analyzing data, prototyping) rather than treating AI as something other people build
Define and track metrics that matter: accuracy, latency, cost per query, user trust, adoption, not just "did we ship it"
Run structured experiments (A/B tests, phased rollouts) to validate whether AI features are actually improving outcomes
Manage tradeoffs between model quality, cost, latency, and user experience
Communicate clearly with both technical teams and non-technical stakeholders about what the AI is doing, why, and where it fails
Own the uncomfortable conversations when an AI feature underperforms or needs to be pulled back