Associate Product Manager

Department: Product Engineering

Experience: 1-2 years

• Talk to users regularly. Run interviews, sit in on support and sales calls, watch people use the product, and read the feedback nobody else reads.
• Separate what users ask for from what they actually need, and turn scattered input into a clear, prioritized problem list.
• Bring evidence, not opinions. Every proposal should trace back to something a real user said or did.

• Turn ideas into clickable prototypes using AI tools like Claude, Lovable, or whatever gets you to a real interaction fastest, so users, stakeholders, engineers, and QA can all experience the thing before it's built.
• Design the experience end to end: the main flow, the empty states, the error messages, the edge cases.
• Use AI to multiply your output. Synthesizing research, building internal tools, analyzing data, testing ideas in an afternoon instead of a sprint.
• Help ship AI-powered features, and think hard about what happens when the model gets it wrong.

• Keep a clear, prioritized backlog for your area. Be able to explain why something is next, including what you're not doing and why.
• Write short and plainly: the problem, the user, one success metric, what's in scope and what isn't.
• Work with engineering and QA through backlog grooming, sprint planning, and QA. Answer questions, resolve ambiguity, test the release yourself before it ships.
• Help plan and run launches with the people who need to know: support, sales, marketing, and leadership.

• A portfolio we can open: Projects, tools, prototypes, or products you've built.
• User curiosity: You open an app and can't help asking why people keep coming back to it, and what you'd steal.
• AI fluency: You've gone past basic prompting. You know how to give a model the right context, you've seen where models fail, and you've thought about what to do about it.
• Taste: Shown two versions of a screen, you can say which is better and explain why in terms of the user. You notice layout, hierarchy, spacing, and what a screen does when it's empty or broken.
• Comfort with data: You can pull numbers out of a sheet or a dashboard, spot when something looks off, and back a conclusion with them.
• Clear thinking under ambiguity: You can break a vague problem into something testable.
• Strong communication: You can explain a decision to an engineer, a designer, and a business stakeholder without changing the substance.
• Attention to detail: In the product, and in your own work.

• You've written an eval, or tuned LLM model settings and can say what changed.
• Fluency in Figma, or formal design training.
• Hands-on time with an analytics tool (Mixpanel, Amplitude, Metabase, GA).
• A side project with real users you didn't have to recruit.
• Interest in education, careers, or working with students.