Forward Deployed Engineer (Enterprise)

Work that outlasts a career. We're solving one of the most ambitious problems in AI: building an AI to understand and be fully personalized to humans and organizations.

We're growing like crazy and it's one in a lifetime opportunity to join the next hockey-stick-curve startup before the singularity.

Join a world-class team of engineers building technology that could reshape AI.

We help companies become AI native by recording how work actually gets done and re-organizing the company as an AI first company.

You are the person who makes that real inside the customer. You deploy screenpipe, learn how the team's work actually happens, and build the system that changes it.

Every enterprise deployment teaches us what to build next. You are the shortest path between a customer's real problem and our product roadmap.

What you'll own

• Enterprise deployments end to end: installation, capture policy, privacy boundaries, security review, rollout, and adoption.
• Sitting with customer teams, mapping repeated workflows, and identifying what should be automated.
• Turning deployments into product: what you build twice should become something we ship to everyone.
• Acting as the technical voice on sales calls - and saying no when the fit is wrong.
• Producing evidence-backed outcomes: time saved, workflows automated, adoption, reliability, and expansion.

You

• You are a strong engineer who would rather work inside the customer's problem than inside a backlog.
• You have shipped software into a real company and watched people use it - as an FDE, solutions engineer, consultant, founder, or product engineer.x
• You can communicate with the founder and an operations manager on the same day.
• You can move from discovery to prototype to production without waiting for a committee.
• You take privacy seriously. We record work; getting the boundaries right is part of the product.
• You are in San Francisco or explicitly ready to relocate before starting.

Why now

Enterprise and product-led growth are the same learning loop for us. Individual users discover unusual workflows; companies show us which outcomes are valuable and repeatable.

You will sit directly in that loop with a product nobody else has: local-first capture of what actually happened at work, ready to power the AI agents companies already use.

screenpipe is YC S26, a team of two, and early enough that your customer work will define the product.

• 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.

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.

1. 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?
2. 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.
3. 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.
4. 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.
5. 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.
6. 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?
7. 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.

We run a desktop app that captures continuously and reliably on people's machines, all day, without getting in the way. That one constraint makes almost everything hard and interesting.

The stack:

A Rust core for high-throughput screen and audio capture, accessibility-tree parsing, and local indexing for fast search. A Tauri desktop app in TypeScript and React, shipping on macOS and Windows. On-device AI. We train and run our own models locally, including PII redaction, so sensitive data never has to leave the machine. Local-first storage and a local API that agents and pipes build on top of. Hard problems we live in: capturing everything without slowing the machine down, running ML on-device across wildly different hardware, turning messy accessibility trees into clean structure, and staying stable enough that people trust us with their entire digital life. We build in the open.

We are training our own privacy model and foundation model to understand human work activity in a modality frontier companies will never risk, which is long term screen recording.

1. 20-minute founder conversation about what you have shipped and why this role interests you.
2. References and final conversation.