Founding Engineer, Product

Full-time. Founding team. New York City, in-person required. Reports to the Co-founder & CTO and works directly with both founders and customers.

Pennant is a YC-backed company building software for corporate governance, starting with proxy voting and company engagement.

Institutional investors, public companies and their advisors make consequential decisions using information scattered across filings, policies, research and conversations. 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.

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.

• 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 behaviour.

• 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,…