Founding Forward Deployed Engineer

About the role

Founding Forward Deployed Engineer

You’re a builder who works at the frontier with our customers. You deploy Nessie into customer environments, build the integrations and workflows they need, and bring what you learn back into the core product. We’re a tiny team with more real customer pull than we can carry, so you’re the technical force that makes deployments real, and you wear the hats a tiny company needs. You are not the sole owner of any customer; the founders lead those relationships, and you make us faster across the board.

What "build" means here

- You have shipped something real: something with users, or something you use every day because you made it for yourself. You have gone from nothing to working software more than once.

- You’re comfortable across the stack. We ship native desktop apps on macOS, Windows, and Linux, a CLI and background daemon, agent integrations, and a cloud backend, across several languages. You do not need all of them; you need to be the kind of person who picks up what they need.

- You reach for AI tools instinctively (Claude Code, Cursor, Codex) and you are faster because of it.

What you will work on

Core engineering

- Ingestion at scale: a data lake for heterogeneous sources, from ChatGPT and Claude Code transcripts to meeting recorders, notes apps, email, and full agent traces. Raw data first, no lossy summarization upfront. Cross-provider capture is still the hardest unsolved problem in the space.

- Ontology: the live state of a company, not a static schema. Live sources, derived fields, conflict resolution, and authority, so humans and agents can trust the answer and it stays current as new work flows in.

- Self-improving agents: skills as the unit of improvement. Sync a team’s skills across Codex and Claude Code, measure every invocation, mine traces for failures and repeated workflows, and propose reviewable edits back.

- Search and retrieval that makes thousands of sessions and documents queryable in milliseconds, across local and cloud.

- Human-AI coordination: team sharing, permissions, and policy. Hierarchical sharing models, separating what is shared from what is visible, and letting an organization share its thinking without oversharing. This is what turns Nessie from an individual tool into the context layer for a whole organization.

- Agent-facing surfaces: CLI, MCP, and plugins for every major harness, so any AI tool can reach accumulated human reasoning.

- Enterprise readiness: self-hosted and on-prem deployments, SOC 2, and the control plane behind them, as we move into larger and more regulated organizations.

Forward deployment

- Embed with a customer to deploy Nessie into their environment: onboarding, the team-sharing and permissions rollout, integrations, and live troubleshooting, alongside the founders. You shadow first; the founders stay the relationship leads. Increasingly this includes self-hosted or on-prem setups for larger or regulated orgs.