Staff Engineer Test Automation
Shield AI is a U.S. defense-technology company developing mission-autonomy software and autonomous aircraft for military and allied operations.
Funding history
Investors
About Shield AI
Founded in 2015, Shield AI builds Hivemind autonomy software and V-BAT and X-BAT aircraft for operations in contested, GPS- and communications-denied environments. Its current site also presents Aechelon synthetic-reality simulation and Vision Systems detection and tracking products.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will provide technical leadership for automated verification across an autonomy platform. You will define test architecture, validation infrastructure, MLOps quality systems, and CI/CD pipelines. You will build Python automation, ML validation pipelines, scenario-based testing, observability capabilities, and test harnesses across simulation, edge-compute, and hardware-in-the-loop environments.
Requirements
- Typically 8+ years of relevant experience in software engineering, test infrastructure, developer tooling, MLOps, systems integration, or systems verification.
- 5+ years of experience building scalable Python automation frameworks or developer tooling.
- Experience designing test, CI/CD, or MLOps infrastructure across multiple engineering teams.
- Experience validating machine learning systems throughout their lifecycle.
- Experience testing GPU-accelerated Kubernetes infrastructure and workloads.
- Experience validating multi-tenant Kubernetes environments.
- Experience qualifying integrated hardware and software systems.
- Experience testing distributed systems, backend services, APIs, and integrated environments.
- Understanding of asynchronous and concurrent Python programming.
- Experience with package management, reproducible environments, and build systems.
- Experience with observability, analytics, reporting, and root-cause analysis.
- Experience working in Linux-based development environments.
Responsibilities
- Own the technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality.
- Design and maintain scalable test frameworks for autonomy software, backend services, APIs, applications, and distributed hardware.
- Lead functional, integration, regression, system, performance, reliability, and end-to-end testing.
- Build automated ML validation pipelines.
- Establish CI/CD and continuous training workflows with versioning and traceability.
- Develop scenario-based validation for autonomy models.
- Create observability, analytics, and failure-triage capabilities.
- Build Python automation for test execution, reporting, environment setup, and experiment comparison.
- Create test harnesses, simulators, stubs, mocks, and synthetic data capabilities.
- Collaborate on verification strategies and design improvements.
- Develop and govern AI-assisted engineering workflows for testing and failure triage.
Benefits
- Bonus
- Benefits
- Equity