Staff Software Engineer in Test, Code Metal

• Typically 8+ years of relevant software engineering, test infrastructure, or quality engineering experience, with demonstrated Staff-level scope and impact. Equivalent depth of experience is valued over a rigid year count
• Significant experience in systems software, compiler or language tooling, developer tools, embedded software, or low-level execution environments
• Production-grade programming proficiency in Python and C++, plus the ability to read and debug unfamiliar C and Rust code
• Experience designing and scaling automated test frameworks, distributed execution systems, or evaluation pipelines for complex systems software
• Strong understanding of memory models, concurrency, runtime performance, and architecture-dependent behavior across CPU, GPU, embedded, or other constrained targets
• Hands-on experience with Linux, containerized environments, continuous integration platforms, and modern build systems
• Ability to translate ambiguous quality risks into measurable validation plans, explain tradeoffs clearly, and lead technical work across team boundaries
• Strong cross-functional collaboration skills, including partnering with Product to align validation strategies with the product value proposition and customer expectations
• Experience validating AI-assisted or nondeterministic software systems, with sound judgment about AI limitations, reproducibility, independent test evidence, and when deterministic controls or human oversight are required

Desirable

• Code Metal is strengthening Quality Engineering across its code-transformation platforms, language toolchains, and AI-assisted engineering workflows
• Our teams already have meaningful validation practices in place
• As the first dedicated Software Engineer in Test (SDET) hire for this space, reporting to the Director of Quality Engineering, you will build on those foundations - identifying gaps, establishing scalable practices, improving consistency and coverage, and creating clearer quality signals and trustworthy release evidence in partnership with Engineering, Research, and Solutions
• Assess existing validation practices, identify material gaps, and establish scalable quality strategies for code-generation, transpilation, and software-transformation workflows
• Strengthen quality practices for AI-assisted and agentic workflows, with sound judgment about reliability, reproducibility, risk, and appropriate oversight
• Build automated quality and regression infrastructure for AI-assisted software workflows that produces reproducible engineering evidence
• Strengthen and standardize quality signals and release evidence, including regression trends, failure attribution, validation results, and documented readiness criteria
• Diagnose complex failures across C/C++, Rust, Python, build systems, generated code, and heterogeneous target environments; make focused corrections where practical and partner with owning teams on deeper fixes
• Integrate validation suites and quality gates into delivery workflows while collaborating with, rather than replacing, the team responsible for shared CI/CD and developer infrastructure
• Lead quality practices for this space, establish technical standards, mentor engineers, and help shape the Quality Engineering function’s roadmap, operating model, and cross-team interfaces