Staff Software Engineer, Quality Engineering

WHO WE ARE

GridUnity is the industry leader in interconnection life cycle management, delivering cutting-edge software solutions that eliminate bottlenecks in the interconnection process and accelerate the transition to a more affordable, sustainable, and resilient energy future.

Our Mission

We unite all grid interconnection stakeholders on a single platform that converts centralized data into actionable intelligence and accelerates the transition to a reliable, sustainable, and cost-efficient electric grid.

GridUnity is a trusted partner to leading utilities, grid operators, and policymakers. Our technology solutions tackle the interconnection queue backlog by significantly reducing processing times. Whether cutting transmission interconnection timelines from years to months or enabling distribution applications to be processed within a day, we help accelerate energy projects at every scale.

Our Vision

Energizing the planet with a faster, smarter, and more sustainable grid.

What you'll do

GridUnity spends its days removing bottlenecks from the interconnection queue. This role removes our bottlenecks.

We are not hiring you to test our software. We are hiring you to build the system that makes "is this safe to ship" a fact we already have, not a meeting we still need.

Being right and being fast pull against each other, and at our scale that tension is an engineering problem rather than a process one.

As the Staff Software Engineer, Quality Engineering, you will own and lead GridUnity's quality engineering strategy: the technical vision and roadmap for the operating model as it scales, the standards it holds engineering to, and what counts as evidence that something is safe to ship.

GridUnity engineering runs on an AI-native, artifact-first model. Agents draft tests from specs, run executions, and write automation. Humans arbitrate the evidence, own the release verdict, and continuously calibrate how much of the agents' judgment to trust. Version one of that system is live today on our flagship product line. You will take ownership of it, make it hold under real delivery pressure, and scale it across every product line.

You will make quality a property of the engineering system itself: honest release gates, a defensible and deliberately shrinking catalog of human-judgment testing, and evaluation mechanisms that tell us exactly where human attention is needed.

You report to the Director of Engineering Operations and partner closely with Product Engineering, Platform Engineering and DevOps to improve release confidence while accelerating delivery.

Where we are, and where you come in

Running now: the quality operating model is documented and live on our flagship product line. An agent-drafting workflow ships review-ready tests against documented why-manual criteria, enforced in the ticket lifecycle. A daily regression cadence runs green, with manual coverage held where human judgment is required. Evidence-backed release gating is live in our test-management system of record. An operations console harvests all of it nightly.

Yours to build: making all of that hold under real delivery pressure, and taking it from the flagship line to the rest, and to the lines that do not exist yet.

What you'll own

The release verdict, as a mechanism rather than a meeting. Execution records as evidence, exit criteria agreed before the deadline exists, a waiver path that blocks nobody but puts a name on the exception and counts it, and a defined escalation route, on the record, for the rare call those criteria do not cover. The mechanics exist; making them hold by design rather than by willpower is the work.

The agent pipeline. Operate and harden the system where agents generate, maintain, and run coverage across API, UI, integration, contract, performance and end-to-end, directly from specs and scenario contracts. Nothing becomes coverage until a person reviews and activates it, and you own where that bar sits.

The review loop. Drafts arrive continuously from engineers, product, and agents, and the review SLA is a promise to the whole company. Keeping it as agent output grows is a design problem, not a throughput one.