Staff Test & Release Engineer, Inference CloudLocation: Santa Clara, Ca / Remote, US
Role OverviewThe 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.
- Lead the move from a single platform-wide release to independently released components, defining the component breakdown, cadence, versioning, and compatibility and upgrade testing with platform architects.
- Define release gates covering functional tests, upgrade and compatibility tests, model accuracy evals, and performance baselines (time to first token, time per output token, throughput).
- Partner with QE, product, platform, and infrastructure teams on a shared test strategy, and push for acceptance criteria specific enough to test.
- Enable feature engineers to write and own tests for their own work.
What You Will Bring- 6+ years in test infrastructure, software engineering in test, release engineering, or a closely related role, including owning end-to-end testing or releases for a distributed system.
- Track record of building test or release automation that measurably improved release reliability or speed.
- Experience running test and release infrastructure across on-premises clusters and a public cloud, preferably Azure (AKS, Azure DevOps or GitHub Actions on Azure, Azure networking and identity).
- Strong Kubernetes experience, including deploying and debugging multi-service workloads, Helm, operators and custom resources, and automated test cluster setup and teardown.
- Experience designing CI/CD pipelines (such as GitLab CI, GitHub Actions, Buildkite, or Argo), including artifact versioning, environment promotion, and rollback.
- Proficiency in Python and pytest or a comparable framework, plus fluency in a second language sufficient to read and test the code under qualification (much of our orchestration layer is Rust).
- Experience testing on shared, limited hardware such as GPU or accelerator fleets, accounting for scheduling, contention, and hardware faults.
- Sound judgment about what to test end to end versus lower in the stack, and the ability to explain tradeoffs to code owners.
- Record of building consensus across teams and turning input into a strategy people follow.
- Fluency with AI and agentic tools for writing tests, triaging failures, and searching logs and code, with the discipline to verify their output.
Preferred Qualifications- Experience testing or releasing LLM serving systems such as vLLM, SGLang, or TensorRT-LLM, including accuracy evals (such as GSM8K, HumanEval, MMLU) and performance benchmarks as release gates.
- Familiarity with speculative decoding, disaggregated serving, or heterogeneous inference.
- Hardware-in-the-loop testing experience, including qualifying software across driver, firmware, and hardware versions.
- Experience with operator and controller testing tools such as envtest, kuttl, or chainsaw, and with upgrade and CRD version-migration testing.
- Experience shipping software into customer-managed, on-premises Kubernetes environments, including compatibility matrices and customer install packaging.
- Experience with canary or progressive rollouts for customer-facing services.
- Rust experience.
- Startup experience or comfort in environments where priorities shift quickly.
