Staff Software Engineer in Test (Modeling and Simulation)

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 .

About the Role

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.

Core Responsibilities

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• Experience validating simulation, geospatial, event-driven, or other stateful systems, including reproducible deterministic and probabilistic behavior.

• Familiarity with testing or debugging C++ simulation or systems code.

• Experience testing TypeScript and React applications with Playwright or equivalent tools.

• Experience with performance testing, generated test data, and API or schema validation.

• Experience testing cloud-based, containerized systems using logs, metrics, traces, and retained test artifacts.

• Familiarity with constructive simulation, distributed simulation standards, or mission-analysis software.

• Hands-on experience evaluating AI-assisted or agentic systems, including tool-calling or multi-step workflows, and understanding risks such as nondeterminism, unsupported outputs, prompt or context manipulation, unsafe tool use, hidden state, and silent failure.

• Experience using AI coding assistants or agents throughout the software development lifecycle and defining quality controls for AI-assisted changes, such as scoped workflows, independently verifiable tests, human code review, CI checks, and risk-based release evidence.

• Experience in a regulated, high-assurance, or mission-critical domain.

• Eligible to obtain and maintain a U.S. Secret security clearance.

Experience level

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.

Benefits

• Pay depends on experience, but we strive to be at the upper end of the salary range

• Health care plan with 100% premium coverage, including medical, dental, and vision

• 401k with 5% matching

• Paid Time Off (uncapped vacation, plus sick and public holidays)

• Flexible hybrid or remote work arrangement

• Relocation assistance for qualifying employees

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