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d-Matrix is hiring a Director of Software Quality Engineering to lead a team of eight engineers responsible for ensuring the entire AI inference stack—firmware, compiler, runtime, and serving—works correctly, automatically, and continuously.
The role is hands-on and strategic. You will own quality strategy across all layers of the stack, design test frameworks and harnesses that engineers reuse, build automated functional/integration/end-to-end test suites, implement accuracy and performance regression detection, define release readiness criteria, and lead defect triage and escape analysis. You'll also grow the team, set testing standards across engineering, and establish CI/CD promotion gates that teams respect.
What makes this role distinctive: quality here is not a gate at the end. Correctness is numerical (quantized inference doesn't produce bit-identical results—you define "close enough"), performance is a correctness criterion (latency regressions are bugs), the target platform changes continuously (new silicon, firmware, compiler updates), test capacity is finite and physical (accelerators are scarce), and failure attribution crosses layers (a red test could be compiler, firmware, board, or flakiness). You'll design test strategy under real resource constraints with deliberate tiering: unit, simulated, single-card, multi-card, full rack.
The team has eight engineers in place with real test content and institutional knowledge but has been without permanent leadership. Your first job is honest assessment: what exists, what to keep, what to retire, where gaps are, then reset strategy the rest of engineering believes in. Automation-first is the mandate—manual effort belongs in exploratory testing and new-feature bring-up only.
This is NOT: building a large manual test organization, outsourced test vendor strategy, post-silicon hardware validation (firmware validation is in scope; silicon bring-up is not), or writing unit tests on behalf of dev teams (developers own theirs; you set standards and provide tooling).
REQUIREMENTS:
- Experience leading a software quality or test engineering team while staying hands-on (writing framework code, reviewing tests)
- Track record taking over existing teams and changing direction, growing engineers into new skills
- Strong programming in Python and/or C++; built test frameworks, not just test scripts
- Systems software testing depth: firmware, compilers, runtimes, drivers, distributed systems, or comparable (testing things without a UI)
- Hardware-software integration testing and failure modes with real devices
- Test strategy design under resource constraints (cannot run everything on every commit)
- CI integration, promotion gates, release gates that teams respect
- Data-driven approach to coverage and risk; judgment to know when a metric measures the wrong thing
- Credibility with developers; function succeeds by making engineers faster and more confident, not blocking them
PREFERRED:
- ML or inference domain knowledge: quantization, numerics, model accuracy evaluation
- Familiarity with inference serving stacks and benchmarks
- Kubernetes-based test environments
- Hardware-in-the-loop testing, device farms, lab automation
- Practical, skeptical experience applying AI to test generation, triage, or failure clustering
