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 is funded by Accel, Salesforce Ventures, B Capital, Smith Point Capital, J2 Ventures, Shield Capital, Overmatch, RTX, and others.
Learn more at codemetal.ai.

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.