Grants Engine: Engineering Manager

Child Poverty Action Lab / Full-time / Remote

The Grants Engine is an AI-powered tool that helps public agencies and community organizations find, prioritize, and pursue federal and state funding. It combines a continuously updated grant clearinghouse built from automated scrapers, AI-driven synthesis of long funding notices into the eligibility criteria that actually matter, recommendations based on organizational profiles and priorities, and grant pipeline and cohort management tools. Our goal is to put public funding within reach of organizations that do not have grant‑writing staff.

We are live with beta users, onboarding our first statewide partner, and moving toward general access, with rapid state‑by‑state expansion of grant coverage.

Today the product is built by two contracted development firms (application and data ingestion), an infrastructure engineer, and part‑time support from CPAL’s centralized engineering team. Automated testing and regression testing, standardized deployment, logging, and monitoring are now in place across the application and scraper pipeline.

What we do not have is a dedicated owner for this layer. With work split across vendors, no single person holds the architecture and technical quality of the whole system, and downstream impacts of one team’s decisions surface later than they should.

As our first engineering manager, you will bring technical ownership in‑house: assess what is actually in place, decide what to keep, what to deepen, and what to replace, and then own it from there.

• Own technical implementation of the product roadmap end to end, from scoping through delivery.
• Partner with the Product Lead to scope features, size effort, and plan resourcing. Surface downstream impacts and tradeoffs early, in terms non‑engineers can weigh.
• Translate partner commitments (data integrations, custom configuration, API work) into realistic technical plans and timelines.
• Own the architectural direction of both the application and the data ingestion pipeline, and make sure the two are designed as one coherent system rather than two products that happen to share a database. Set and enforce the standard for what “done” means (test coverage, observability, documentation) through design discussions and code review.
• Extend our testing practice into the evaluation infrastructure our AI workflows require. A wrong summary can cost an organization weeks on an ineligible application.
• Own our engineering practices (testing, deployment, logging, monitoring): deepen coverage, close the gaps, and make sure they are consistently followed rather than nominally in place.
• Build operational discipline: a real cadence for reviewing logs and alerts, clear ownership when something breaks, and the habit of catching problems before users report them.
• Serve as the single point of technical accountability across our contracted and internal engineers. Work with the Product Lead to set shared standards and communication cadence, and hold contracted partners to them.
• Help define, recruit for, and hire the engineering roles we add as we scale, including deciding where contracted work should move in‑house. Onboard and mentor engineers, and raise the technical bar of everyone around you.

The Engineering Lead (EL) reports to the Product Lead (PL). The PL owns roadmap strategy, prioritization, and phasing, and is accountable for what we build and why. The EL owns how we build it and is accountable for technical quality, architecture, and delivery.

This is a reporting line, but the working relationship is a partnership. The EL is expected to push back on sequencing, scope, and timelines when the technical reality warrants…