Founding Engineer, Data & AI

Pennant AI; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Pennant AI is hiring for a founding backend/data-focused engineer based in New York City. The role involves building and owning the data systems that make Pennant useful and trustworthy, including production-grade pipelines and versioned evidence. You will work closely with founders and domain experts to shape the tech stack and governance of data.

Qualifications

• Experience building reliable backend data systems and pipelines.
• Strong understanding of schema evolution and data provenance.

Responsibilities

• Build and own data systems that are robust and scalable.
• Develop and maintain production data pipelines and asynchronous jobs.
• Evolve databases and interfaces without breaking consumers.
• Work with messy filings and customer documents to extract structured information.

Skills

Backend engineering

SQL & Postgres

Asynchronous processing

Document-driven data pipelines

Model-based systems

Codebase navigation (TypeScript/Go)

Tools

TypeScript

Go

Postgres

BigQuery

Job description

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

About Pennant

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.

The Role

Build and own the data systems that make Pennant useful and trustworthy.

This is a backend- and data-intensive applied AI role. You should be as comfortable debugging a production pipeline and evolving a database schema as evaluating a document-extraction model. You will build on an existing codebase, working with messy filings and customer documents and making the results dependable enough for customers to use. The work includes asynchronous jobs, versioned evidence and safe reprocessing, not just prompts and model experiments.

Your first mandate is one prioritized pipeline and its downstream use. You will establish what good looks like with domain experts, improve the system, and own it in production. As one of our first engineering hires, you will also help shape how we build, test and operate software.

What You Bring

• Strong backend engineering, SQL and Postgres fundamentals, with experience operating production services or data pipelines.
• Practical experience with asynchronous or distributed processing: idempotency, retries, leases and fencing, concurrency, and recovery when only part of a job succeeds.
• Experience turning messy documents or other imperfect source data into structured information people depend on. Here that means public company filings, third-party data feeds and customer spreadsheets.
• Practical experience shipping model-based systems and improving them through evaluations against human-reviewed references, user feedback and failure analysis, with the observability to explain what a model did on a given input.
• Sound judgment about schema evolution, entity resolution, historical records and data provenance.
• The ability to become productive in an existing codebase, debug across services and storage systems, and evolve interfaces or schemas without breaking their consumers. Our services are TypeScript and Go over Postgres and BigQuery.
• The independence to narrow an ambiguous problem, ask for the context you need and carry the work through production use.

Experience with financial or legal documents and human-review tools is useful. Governance expertise and model-training research are not prerequisites. We care more about systems you have made reliable than a particular language, model or framework.

How We Work

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.