Full-time. San Francisco, in person. Base salary: $160,000 to $220,000 per year. Equity: 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 does not offer visa sponsorship. ## Make computer context useful in everyday work Screenpipe helps companies find work worth automating. It captures screen and audio activity, makes computer history searchable, and gives AI agents context from how work happens. Capture history stays on the device by default; optional cloud AI, sync, and enterprise storage have separate data paths. You'll work directly with the founder and engineers to turn a powerful technical product into something people can understand and use repeatedly. You'll own the path from discovering a customer problem to shipping a change and checking the result. ## What you'll own - Talk to users and watch them work. Use interviews, support conversations, and product data to identify where the product helps and where people get stuck. - Improve the journey from installation to a useful result and repeat use. Make setup, permissions, and the product's capabilities clear. - Decide what to build next and what can wait. Turn the problem into a clear flow, prototype, and scope that engineers can implement. - Stay involved through implementation, release, and customer follow-up. Investigate when a shipped feature fails to solve the original problem. - Define activation and retention measures, check the instrumentation behind them, and run focused experiments. Look for evidence that users got useful work done and came back. - Treat privacy and control over captured data as part of the product experience. ## What we're looking for You've owned a software product from discovery through delivery and can explain your contribution and what happened after launch. You have strong UX judgment and test assumptions with customers. You can work with funnels and cohorts, question how a metric is defined, and connect the numbers to what users tell you. You use AI to investigate problems and build prototypes, and can discuss technical tradeoffs with engineers. This is hands-on product work in an early-stage company. You'll need to make decisions with incomplete information, communicate clearly, and take responsibility for the outcome. Experience with desktop software, developer tools, or AI agents is useful. The role is in person in San Francisco, with direct access to users and a public codebase you can explore. ## How we work Each role has a scorecard with clear objectives and measurable targets, so you know what you're responsible for and what success looks like. We review it together every two weeks, look at the results and what's getting in the way, and agree on the next priorities. You'll own how you reach those goals and speak up when the evidence calls for a different approach. We work long days, and weekend work is part of our pace. We want you to understand that expectation before joining. We also make time to celebrate wins together and recognize the work that got us there. ## 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. ## Apply Keep answers concise. Links and bullets are welcome. Include your current location and when you could start working in person in San Francisco. Use public or anonymized examples, and remove credentials, private prompts, and confidential customer or employer information. 1. Share a product or feature you owned. Explain the customer problem, your contribution, a customer insight that changed what you shipped, and what happened afterward. Include how you measured the result. 2. Show an AI workflow you use repeatedly. Explain your models, tools, memory or integrations, what you configured yourself, how you check the output, and what still fails. Include approximate weekly token usage or spend, separating your usage from a team total, and what it produces. A sanitized setup excerpt or short demo is welcome. 3. What are you learning now? Share a book or idea that changed how you work, your two or three favorite books, and roughly how many you read or listened to in the past year. Include an example of feedback that changed your mind. 4. What would you investigate first at Screenpipe, and why? Include something you built or improved on your own initiative. Explore the product at https://screenpipe.com and the codebase at https://github.com/screenpipe/screenpipe, then apply through YC.