Full-time. Founding team. New York City, in-person required. Reports to the Co-founder & CTO and works directly with both founders and customers.
About the company
We bring that information together so teams can understand the evidence, apply their own judgment and preserve why they made a decision.
Our ambition is a world model for corporate governance: a system that connects institutional knowledge, policies, decisions and outcomes. Getting there starts with reliable data and software customers trust in their daily work.
Responsibilities
Own a customer workflow from understanding the problem through repeated production use.
This is a full-stack engineering role with product judgment and customer ownership. You will build on an existing application, connecting interfaces, backend APIs, persistent state and integrations into complete workflows. You should be comfortable tracing a user action from the browser to the database, and owning what happens when only part of that sequence succeeds. You will work directly with customers to observe how they work, challenge assumptions, prototype interactions and follow up after release.
Your likely first assignment is a focused research or company-engagement workflow: help an analyst review evidence, make a decision and follow through. The goal is a useful, reliable sequence, not a collection of disconnected screens or an entire CRM built before a customer can use it.
As one of our first engineering hires, you will have a substantial say in the product and how we build it. Your responsibility includes onboarding and production support, not just shipping the first version.
What you'll own
Work with founders and customers to turn an ambiguous request into a bounded problem. Agree on what success means, make tradeoffs explicit and build the smallest complete workflow that addresses it.
Build across the interface, backend APIs, database transactions and integrations. Trace existing behavior before changing it, evolve contracts safely, and connect working components rather than defaulting to a rewrite.
Make dense evidence easy to inspect and act on. Use prototypes and customer observation to improve navigation, source review and decision capture. Show uncertainty and incomplete coverage without making the interface overwhelming.
Build useful AI features, such as evidence-backed research summaries. Evaluate their behavior on representative cases, use deterministic code where it works better, and make consequential actions subject to appropriate human review.
Preserve decisions, corrections, rationale and approval history. Enforce permissions and make imports, retries and partial failures recoverable without duplicate records or lost work. Make saved, pending, unavailable and demonstration states clear to users.
Own onboarding and follow-through for your workflow. Measure completion, repeat use and manual effort. Build the internal tools needed to support customers without turning every request into a founder intervention.
You will work closely with our data and AI engineering counterpart, who owns source ingestion, extraction and the underlying data-quality contract. You own the application workflow and how customers inspect, correct, approve and use its outputs.
When a customer encounters a problem in your workflow, you own getting it resolved, bringing in the appropriate engineer rather than fixing every component yourself. Together, you will define how data changes affect customer corrections and approvals. We will scope your initial work so you can make progress even if the other role has not yet been filled.
The founders remain responsible for company priorities and commercial commitments. Founders and domain reviewers provide policy interpretation; you will help translate that expertise into clear product behavior.
Problems you might tackle
Let an analyst inspect a recommendation alongside its evidence, correct a fact and understand whether the change requires a new review.
Turn imported engagement notes into a useful company history, then help the analyst prepare for the next conversation and capture what was decided.
Recover a partially failed import without duplicating records. Show the customer what succeeded, what still needs attention and how to continue.
The role may later extend to reporting, voting execution or other governance workflows. We will prioritize those against customer needs and external dependencies rather than treating them as simultaneous first assignments.
Your first 90 days
By day 30: Observe a selected customer's workflow, agree on the initial scope and acceptance criteria, and ship a useful improvement. Establish a baseline for completion time and manual effort.
By day 60: Own the scoped workflow through onboarding and production use. Test permissions and recovery paths, instrument usage, and add evaluations for model-based behavior.
By day 90: Carry a customer request through to accepted production use without a founder coordinating each step. Show evidence of repeat use where the workflow's cadence permits, explain the remaining friction and identify the next improvement from observed behavior.
We will choose the initial customer and measures together. A shipped feature is a starting point; success includes whether the intended user can complete the task reliably.
What you bring
Strong full-stack fundamentals in TypeScript: component-based web applications (we use React and Next.js), typed backend APIs (we use NestJS), Postgres and data modeling.
Product and interaction judgment. You can make a complex workflow understandable, notice consequential details and use a prototype to test an assumption before overbuilding.
Sound judgment about database transactions, durable application state and integrations. Our product is multi-tenant, so you take organization-scoped authorization seriously and test it.
Experience testing complete user journeys and failure recovery, not only isolated components. We run browser tests with Playwright against the real API.
The ability to talk directly with customers, distinguish the underlying need from the requested feature, and explain technical tradeoffs without hiding behind jargon.
Practical familiarity with LLM-powered products such as streaming chat, tool calling and evidence-backed summaries, and a willingness to evaluate their usefulness and failure modes. Deep experience shipping complex, data-intensive products can outweigh a long list of AI projects.
Experience with financial products, document viewers, approval workflows or early-stage companies is useful. We will teach the governance domain. We care more about what you have made work for customers than which framework you used.
Our stack
TypeScript end to end: Next.js and React with Tailwind on the web, NestJS on the API, Postgres for application data, with Go for some backend services. We run on Google Cloud and Vercel, test with Vitest, Jest and Playwright, and CI enforces coverage gates. Observability is Datadog and Sentry.
Conditions
We work in person in New York and stay close to customers. We prototype quickly, use AI development tools where they help, and remain responsible for what we ship.
We narrow scope before compromising correctness, permissions or customer trust. We test representative cases and failure paths, observe what happens after release, and flag risks early. Founding engineers have room to make decisions and are expected to make their reasoning understandable to the team.
Apply
Send a short note and examples of work you have shipped. Describe a customer problem you owned, what you built across the stack, what changed after users tried it and how you knew it worked. Links, screenshots or a short walkthrough are welcome; do not share confidential customer material.
Skills: Google Cloud, Node.js, PostgreSQL, React, TypeScript, SQL, Next.js, LLMs, AI Agents, Generative AI, NestJS, Vercel, Playwright, OpenAI, LangSmith, Datadog