Staff Software Engineer in Test, Compiler & Systems Validation
First dedicated SDET hire at Code Metal, building scalable validation, regression, and quality-gate infrastructure for AI-assisted code-generation, transpilation, and software-transformation platforms. Day to day: assess quality gaps, build reproducible test/CI evidence, and diagnose failures across C/C++, Rust, Python, and heterogeneous targets.
- Compensation
- $153K–$183K / yr
- Location
- San Francisco Hub
- Work setup
- Hybrid
- Seniority
- Staff
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- 8+ years of relevant software engineering, test infrastructure, or quality engineering experience with Staff-level scope
- 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 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 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 and lead technical work across team boundaries
- Strong cross-functional collaboration skills, including partnering with Product on validation strategy
- Experience validating AI-assisted or nondeterministic software systems with sound judgment about AI limitations and reproducibility
Nice to have
- Experience improving and scaling quality practices for complex, nondeterministic, or high-consequence software systems
- Experience evaluating agentic or AI-assisted systems and translating their 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 such as defense, aerospace, automotive, medical devices, industrial systems, or semiconductors
Full description
About Code Metal
Code Metal is the leader in automated software engineering you can trust. As AI writes more of the world's code, the bottleneck in software has shifted from writing code to verifying it works, and AI cannot verify its own work with certainty. Code Metal takes a fundamentally different approach: constrain AI to what it does reliably, verify every step independently of the model using formal methods, and keep engineers in the loop on the decisions that matter. The result isn't code that probably works — it's code that is provably correct, with auditable proof. Customers including the U.S. Air Force, L3Harris, RTX, and Toshiba use Code Metal to modernize legacy code, optimize performance on real hardware, and move prototypes to production, fast. Founded in 2023 with offices in Boston and San Francisco, Code Metal isfunded byAccel, Salesforce Ventures, B Capital, Smith Point Capital, J2 Ventures, Shield Capital, Overmatch, RTX, and others.
Learn more at .
The Role
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.
Core Responsibilities
- 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.