Product Manager

About Leptex

Leptex is building personal intelligence for everyday people. Our mission is to democratize AI: make it something everyone can use, understand, and control. Each person should decide how their data is used, what their AI can do, and which decisions remain theirs. We start with farmers and independent business owners, building alongside them and learning from their daily work. Join us to bring that power to more people.

About the role

As a Product Manager at Leptex, you work directly with the founder and turn a real customer problem into a working product. You personally research the need, design the UI and UX, use AI tools to write and revise code, and build, test, and deploy a minimum viable product (MVP). You own the work across product, design, and implementation, with the independence to deliver an MVP without relying on a separate engineering team. This is a full-time remote role, and you must live and work in the United States.

We start with farmers and independent business owners. Your product decisions should reflect their available time, budget, and technical confidence. Personal AI sovereignty must translate into practical controls: clear choices about data, understandable permissions, ways to correct mistakes, and the ability to take back control. You will test these experiences with users, learn what works, and keep improving the product yourself.

Compensation: Cash compensation has not yet been determined. Compensation and any applicable benefits will be set out in a written agreement before work begins.

What you'll do

• Understand real work: Speak directly with farmers and independent business owners, observe their daily work, and try the workflows yourself. Find where inquiries, orders, information, or decisions get stuck, and understand the time and resources people can actually spare.
• Define problems and priorities: Turn observations into a clear problem, a focused product scope, and measurable success criteria. Decide what to build first, explain the trade-offs, and keep the roadmap tied to evidence from customers.
• Design the UI and UX: Create user journeys, information architecture, wireframes, visual designs, and interactive prototypes yourself. Make onboarding, everyday tasks, and AI controls clear, accessible, and usable on both phones and computers.
• Build working MVPs: Use AI coding tools to write, understand, and debug code, connect the necessary data and services, and turn your designs into a working product. Test and deploy the MVP yourself, learning unfamiliar technical skills as the work requires.
• Make personal AI sovereignty usable: Design how people see, correct, share, and remove their information; grant or withdraw permissions; and review important actions. Define and check the system behavior that enforces those choices, including what happens when someone says no or changes their mind.
• Shape AI behavior: Develop and refine prompts, knowledge sources, memory, and tool workflows around a specific user need. Define what the AI may answer or do, how it handles uncertainty, and when it returns a decision to the person.
• Evaluate quality and safety: Build representative test cases and failure scenarios, check answers and actions, and investigate errors. Validate data boundaries, permission changes, and human handoffs with synthetic or appropriately de-identified material before running controlled customer trials.
• Run pilots and improve adoption: Help users get started, watch where they hesitate, and follow up after the first session. Improve the product, explanations, and defaults until people can complete useful tasks and manage their AI with less help.
• Measure practical value: Track completed tasks, repeat use, time saved, corrections, support needs, and the cost of providing the service. Use interviews and product evidence to decide what to improve, stop, or expand while preserving user control.
• Own delivery and reuse: Carry changes through implementation, release, and follow-up, including fixing problems you uncover. Turn recurring needs into maintainable product capabilities, document important decisions, and coordinate with the founder and relevant specialists when a question needs deeper expertise.

What we're looking for

• Exceptional learning ability: This is our highest priority. You quickly develop a deep understanding of unfamiliar AI capabilities, design methods, technical tools, and business problems. You find reliable sources, test your understanding, and can show how you turned new knowledge into a working result.
• Practical AI product experience: You have built or materially improved an AI product that people have used. You can explain the problem, your contribution, what failed, and what changed after user feedback. Professional work, independent projects, and open-source projects can all demonstrate this experience.
• AI tool fluency: You use AI assistants, coding agents, design and editing tools, and automation in your own work. You know how to provide useful context, inspect results, identify mistakes, and improve the output. You are ready to learn new tools as capabilities change.
• UI and UX design: You can design an experience from user flows and information structure through visual details and interactive prototypes. You make sound choices about hierarchy, language, accessibility, and mobile behavior, and validate them with people who have little technical experience.
• Coding and independent implementation: You can use AI tools to build a functioning MVP, including the interface, data handling, and necessary integrations. You can read, change, and debug the code, run tests, and deploy it yourself. You are willing to learn the languages and frameworks the product needs and take responsibility for the result.
• AI systems judgment: You understand how language models, retrieval, memory, tools, and access controls affect product behavior. You can reason about incorrect answers, unauthorized actions, latency, and cost, and recognize where reliable system controls and human judgment are needed.
• Commitment to personal AI sovereignty: You care about ordinary people having control over their own AI, data, and important decisions. You can turn that conviction into understandable choices, limited permissions, and ways to correct or stop the system, then check whether those controls work in practice.
• Practical product judgment: You listen closely to users, understand the constraints of a small business, and distinguish a useful improvement from an impressive demonstration. You set priorities based on evidence, define meaningful outcomes, and revise your view when real use contradicts it.
• Independent ownership and clear communication: You can turn an unclear problem into a usable product with limited resources. You organize your time, make progress across research, design, and development, explain trade-offs plainly, and seek specialist help when you reach the limits of your expertise.
• Education: No degree or field of study is required. We welcome people from every discipline. What matters is your ability to learn, use AI tools, and build products people can actually use.

Equal opportunity

Leptex is an equal opportunity employer. We base hiring decisions on job-related skills and qualifications. We do not discriminate against applicants or employees based on race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable law.

If you need a reasonable accommodation during the application or interview process, write to careers@leptex.com.

We do not use AI to screen or assess applicants. A person reads every application. We use application materials to assess your suitability for a role, communicate with you about recruitment, and meet applicable legal and recordkeeping obligations. We retain them only for as long as needed for these purposes, including any required retention period or legal hold, and then delete them. For questions or requests about your application data, contact privacy@leptex.com.