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Code Metal is an automated software engineering platform that uses formal methods and AI to produce provably correct code. The company serves defense and aerospace customers (U.S. Air Force, L3Harris, RTX, Toshiba) and is backed by Accel, Salesforce Ventures, and others.
As the first dedicated Staff Software Engineer in Test (SDET) for the quality engineering function, you will report to the Director of Quality Engineering and establish scalable validation practices across Code Metal's code-transformation platforms, language toolchains, and AI-assisted workflows.
Key responsibilities include:
- Assess existing validation practices and identify gaps in 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, and risk
- Build automated quality and regression infrastructure that produces reproducible engineering evidence for AI-assisted software workflows
- Strengthen quality signals and release evidence, including regression trends, failure attribution, and validation results
- Diagnose complex failures across C/C++, Rust, Python, build systems, and heterogeneous target environments
- Integrate validation suites and quality gates into delivery workflows while collaborating with CI/CD and developer infrastructure teams
- Lead quality practices, establish technical standards, mentor engineers, and shape the Quality Engineering function's roadmap and operating model
This role sits at the intersection of compiler validation, AI systems testing, and formal verification—addressing the core challenge that as AI writes more code, the bottleneck shifts from writing to verifying correctness.
REQUIREMENTS:
- Significant experience in systems software, compiler/language tooling, developer tools, embedded software, or low-level execution environments
- Production-grade programming proficiency in Python and C++; ability to read and debug C and Rust
- 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 (CPU, GPU, embedded, constrained targets)
- Hands-on experience with Linux, containerized environments, CI platforms, and modern build systems
- Ability to translate ambiguous quality risks into measurable validation plans and lead technical work across team boundaries
- Strong cross-functional collaboration skills, including alignment with Product on validation strategies
- Experience validating AI-assisted or nondeterministic software systems, with sound judgment about AI limitations and when deterministic controls or human oversight are required
- Typically 8+ years of relevant software engineering, test infrastructure, or quality engineering experience, with demonstrated Staff-level scope and impact
PREFERRED:
- Experience improving quality practices for complex, nondeterministic, or high-consequence systems
- Experience evaluating agentic or AI-assisted systems and translating risks into practical quality controls
- Deep hands-on experience with compilers, language tooling, intermediate representations, ABI compatibility, or cross-language semantic validation
- Experience establishing repeatable validation environments and traceable engineering evidence
- Familiarity with formal verification and translating research methods into practical engineering checks
- Professional experience in regulated, safety-critical, or high-assurance domains (defense, aerospace, automotive, medical devices, industrial systems, semiconductors)
