Forward Deployed Engineer (Enterprise)

Full-time. San Francisco, in person.

Base salary: $120,000 to $240,000 per year. Equity: 0.25% to 1.00%.

Turn a customer's workflow into a working deployment

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 with customer teams to understand their repeated work, deploy Screenpipe, and build useful automations. You'll stay involved through rollout and adoption, then bring what you learn back into the product.

What you'll own

• Own enterprise deployments from installation through rollout and adoption, including capture policies, privacy boundaries, and security review.
• Work directly with customer teams to map workflows, identify useful automation opportunities, and agree on how to measure the result.
• Build prototypes and take them into production. Debug deployment and integration problems, and follow through when the workflow fails in day-to-day use.
• Turn repeated customer needs into reusable product capabilities. Explain the problem and evidence to the founder and engineering team.
• Be the technical voice in sales conversations. Explain what the product can do, surface gaps, and identify when a deployment is a poor fit.
• Track outcomes such as time saved, workflow reliability, adoption, and expansion. Check that the result is useful to the people doing the work.

What we're looking for

You've shipped software into a real company and watched people use it. Your background might be forward deployed engineering, solutions engineering, consulting, founding a company, or product engineering. Be ready to explain what you personally built and what happened after deployment.

You can investigate a customer's problem, write the implementation, and explain the tradeoffs to both engineers and operations teams. You're comfortable moving from discovery to prototype to production and taking responsibility for issues after launch.

Privacy is part of the engineering work. You should be able to reason about what is captured, where it is stored, who can access it, and what an agent is allowed to do.

Our stack includes Rust, TypeScript, and React. You'll work in person in San Francisco. If you plan to relocate, include when you could start here.

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 deployment or workflow you personally built for a team. Explain the original problem, what you shipped, a production issue you investigated, and the outcome. Include customer feedback that changed your approach.
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 in a Screenpipe customer deployment, 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.