Staff Software Engineer in Test, Code Metal

• Significant experience as an SDET, test-infrastructure engineer, quality architect, or software engineer responsible for automated validation of production systems
• Strong production programming skills in Python
• Deep experience building Python test infrastructure using pytest or comparable frameworks, including asynchronous testing, fixtures and factories, parallel execution, and CI integration
• Demonstrated experience designing risk-based test strategies across multiple layers of a distributed system
• Experience validating complex systems with partially nondeterministic behavior, such as simulation, statistical models, ML, or AI-assisted workflows, and sound judgment about deterministic checks, reference evidence, statistical evaluation, and human oversight
• Hands-on experience testing Docker-based or comparable containerized, multi-service applications, including APIs, databases, messaging, authentication, authorization, and data isolation
• Experience building and improving CI workflows with GitHub Actions or a comparable platform, including test isolation, failure diagnosis, and flaky-test control
• Ability to set technical direction, write design documents, mentor engineers, and lead multi-team initiatives through influence
• 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
• You have turned quality strategy into test architecture for a product area, led that work through design docs and influence (not people management), and shipped the infrastructure and tests yourself

Desirable

• Code Metal is strengthening its Quality Engineering capabilities to make software quality more measurable, repeatable, and actionable
• We are seeking a founding Staff Quality Engineer (SDET) for Modeling & Simulation, within the centralized Quality Engineering function
• Our Modeling & Simulation team builds wargaming and decision-support platform that helps the U.S. Department of War (DoW) evaluate mission- and campaign-level force-design decisions
• The system combines backend APIs, asynchronous workers, geospatial and sensing models, authenticated multi-team play (including fog of war), external simulation integrations, and emerging AI-assisted capabilities
• The platform already has automated unit and integration coverage, but its growing combination of distributed services, stateful simulation, external integrations, probabilistic behavior, and high-consequence user workflows requires a more systematic validation architecture
• This role will connect and extend that foundation into reproducible quality evidence across services, models, and end-to-end workflows
• You will design and build the platform’s automated validation system, defining which behaviors require deterministic verification, statistical or model-based evaluation, or human review, and how limitations and residual risk are communicated as release evidence
• This is a hands-on software engineering role: you will build foundational test infrastructure, write critical tests, and help engineers build quality into their work
• Translate Quality Engineering strategy into a risk-based validation approach for Modeling & Simulation across unit, integration, contract, end-to-end, performance, resilience, and security-critical workflows
• Build reliable test infrastructure, including deterministic fixtures, isolated test data, service and event-contract tests, integration harnesses, versioned reference scenarios, deterministic replay, and focused browser automation for critical user journeys
• Improve CI feedback through effective test partitioning, parallel execution, caching, failure attribution, and flaky-test control, in partnership with shared platform owners
• Write behavior-focused tests for high-risk planning, sensing, simulation, authorization, state-transition, and recovery workflows
• Partner with simulation and model owners to turn intended behavior, assumptions, and known limitations into reusable reference scenarios, regression baselines, characterization or differential tests, and reproducible quality evidence
• Define validation and release criteria for simulation and emerging AI-assisted workflows, combining deterministic assertions, contracts, reference checks, statistical or model-based evaluation, failure-recovery testing, and appropriate human oversight
• Establish repeatable performance and reliability validation with measurable objectives and clear release evidence
• Participate in design and code reviews, identify testability risks early, and mentor engineers in testing complex simulation, distributed-system, and AI-assisted changes without becoming an approval bottleneck
• Turn lessons from Modeling & Simulation into reusable quality bars, reporting practices, testing standards, and validation infrastructure for the centralized Quality Engineering function