Senior Product Manager
Confirmed on the employer's own hiring board on Sep 25, 2026. First seen by Alion on Sep 25, 2026.
San Francisco, CA · Full-time · In person
Screenpipe gives AI agents memory of your work. It keeps raw computer history local by default and makes that context available to the AI tools you choose.
We're looking for a senior PM who can make a powerful technical product simple and useful in everyday work.
You'll work directly with the founder and engineers, owning the path from understanding a customer's problem to shipping a change and seeing whether it helped.
What you'll own
- Customer discovery. Talk to users regularly, watch them use the product, and understand what they are trying to get done.
- The customer journey. Help people move from installing Screenpipe to getting a useful result and making it part of their routine.
- Product priorities. Combine interviews, support feedback and product data to decide what matters next and what can wait.
- Delivery. Turn problems into clear flows, prototypes and scoped work. Stay involved through implementation, release and follow-up.
- Product outcomes. Define meaningful activation and retention measures, run focused experiments, and check whether changes improve real usage.
- Trust. Treat privacy, permissions and clear explanations as part of the user experience.
What we're looking for
- You've owned a software product end to end and can explain what you personally shipped, why you chose it and what happened afterward.
- You have strong UX judgment and know how to test your assumptions with customers.
- You're comfortable with product analytics, funnels and cohorts, including questioning how a metric is defined.
- You can use AI tools to investigate problems and build prototypes, and work closely with engineers on technical tradeoffs.
- You take ownership, communicate clearly and can make decisions with incomplete information.
- You're based in San Francisco and want to work together in person.
Experience with early-stage products, desktop software, developer tools or AI agents is useful.
Why Screenpipe
This is a chance to shape how people use AI with the context of their actual work. You'll have direct access to users, a public codebase to learn from, and room to carry an idea from discovery through to a working product.
Benefits
Health and recovery: Personal health coach or trainer, gym membership, sauna and cold plunge access, and unlimited massages.
Health insurance: Top-tier health insurance.
Flexibility: San Francisco is our home base, with flexibility to work remotely from time to time, including trips abroad such as Thailand.
Learning: Unlimited books and audiobooks, on us.
AI tools: $20,000 per month in AI tokens to build, experiment, and do your best work.
Personal growth: Career and life coaching.
Team travel: Team trips and work retreats, including places like Hawaii.
Compensation
Base salary: $160,000 to $220,000 per year. Equity range: 0.25% to 1.00% on a fully diluted basis. Final compensation depends on experience and role scope. Any equity grant is subject to board approval, equity plan terms, vesting and definitive documents.
This role is full-time and in person in San Francisco. We are not offering visa sponsorship for this role.
Apply
Send us a product you helped build and a short explanation of your contribution. Include one example of a customer insight that changed what you shipped. Please use public examples or anonymize confidential details.
Explore Screenpipe and the codebase, then apply through YC.
How you learn and work with AI
We look for curiosity, independent thinking, direct and thoughtful feedback, and ownership of real user outcomes. We want people who investigate problems, build useful things, and check whether their work actually helped.
When you apply, include short answers to these questions alongside examples of your work. Bullets and links are welcome. Estimates are fine; explain what the numbers represent.
Reading and curiosity: Roughly how many books did you read or listen to in the past year? Which two or three are your favorites, and what idea from one changed how you think or work? What are you learning now?
AI usage: Roughly how many tokens do you use in a typical week, across which models and tools? If your tools do not expose token counts, share your approximate weekly AI spend or plan and usage pattern instead. Separate personal usage from a team's total, and describe what you produced with it.
Your AI setup: Walk us through the setup you actually use: models, coding agents, editors, memory, MCP servers, skills, and automations. Which parts did you configure or build yourself? A sanitized excerpt from your CLAUDE.md, AGENTS.md, or equivalent instructions is welcome.
An unusual workflow: Show one distinctive AI workflow you use repeatedly. Explain its inputs, steps, tools, output, and how you check quality. What did it replace, what improved, and where does it still fail? A short demo, diagram, or concrete example works.
Ownership and user judgment: Describe an ambiguous problem you took from discovery to a shipped result. What did you learn directly from users, what did you decide not to build, and how did you know the result helped? Be clear about your own contribution.
Truth and feedback: Tell us about evidence or feedback that changed a strongly held product or technical opinion. How did you respond, and what changed in your work or collaboration?
Initiative: What have you built or improved because you thought it should exist, without someone handing you a detailed task? What would you investigate first at Screenpipe, and why?
We care about how you learn, exercise judgment, and produce useful outcomes. Share public or anonymized examples only; remove credentials, private prompts, and confidential customer or employer information.