Test & Release Engineer

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.

We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution.  Ready to come find your playground? Together, we can help shape the endless possibilities of AI. 

Staff Test & Release Engineer, Inference Cloud

Location: Santa Clara, Ca / Remote, US

Role Overview

The Inference Cloud Engineering team builds the deployment and orchestration technology behind d-Matrix’s inference services: an internal cloud where teams test and tune models, and a customer-facing Cloud with self-service inference endpoints and compute. Our accelerators run in on-premises clusters, and parts of the internal cloud also run in Azure. As the Staff Test & Release Engineer, you will own how this software is tested end to end and how it is released. When we say a feature is done, an automated test has proven it works as the user story describes, on the hardware it will run on, and the release process can deliver it to customers predictably and roll it back safely. Many workloads span d-Matrix Corsair accelerators and GPUs, and that hardware is scarce and shared, which makes test design challenging. You will build on existing test suites, CI pipelines, and benchmarks, keep what works, and change the rest. You will partner with QE, product, platform, and infrastructure teams to build a test and release system that scales without slowing development. This is not a manual testing, gatekeeping, company-wide CI/CD, or performance engineering role.

What You Will Do

• Design and build end-to-end test infrastructure for Inference Cloud that stands up homogeneous and heterogeneous inference deployments on demand, runs tests against them, and tears them down.

• Turn user stories and acceptance criteria into automated end-to-end tests, including failure cases such as invalid API keys, unavailable endpoints, and failed draft nodes.

• Bring real hardware into CI by running tests on GPU and Corsair nodes, scheduling fairly on a limited shared pool, and owning test-signal quality, including flaky tests and fast routing of failures to the right owner.

• Own the release strategy for the orchestration platform, Kubernetes tooling, and benchmarking tools, including versioning, cadence, branching, promotion, rollback, release notes, and go/no-go criteria.