Staff Automation Test Engineer - Autonomy & ML Ops

Shieldai; San Diego (CA); On-site; USD 140,000 - 180,000; Full time

Job summary

Shield AI is seeking a Staff Automated Test Engineer to lead automated verification for the Hivemind autonomy platform. You will define verification standards, build scalable test infrastructure, and drive MLOps quality across autonomous systems.

The role spans testing for simulation, edge computing, software-in-the-loop, and hardware-in-the-loop environments, with emphasis on ML model validation and end-to-end system integrity.

Qualifications

• 8+ years of software engineering/test infrastructure experience
• Experience building scalable automation frameworks in Python
• Experience validating ML systems across data/training/evaluation/deployment/monitoring lifecycle
• Strong system-design skills for distributed systems and hardware-in-the-loop environments
• Familiarity with ML model validation, CI/CD, and MLOps practices

Responsibilities

• Own the technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem
• Design and maintain scalable test frameworks for autonomy software, backend services, APIs, and distributed environments
• Lead functional, integration, regression, system, performance, and end-to-end testing across simulation, edge, and hardware-in-the-loop setups
• Build automated ML validation pipelines covering data quality, model accuracy, robustness, and deployment readiness
• Develop CI/CD and continuous training workflows with versioning and traceability for datasets and models
• Collaborate with cross-functional teams to define verification strategies and improve designs before implementation

Skills

Python automation

Distributed testing

CI/CD pipelines

Tools

Conan

pip

setuptools

Poetry

Nix

Job description

Shield AI is seeking a Staff Automated Test Engineer to lead automated verification for the Hivemind autonomy platform. You will define verification standards, build scalable test infrastructure, and drive MLOps quality across autonomous systems.

The role spans testing for simulation, edge computing, software-in-the-loop, and hardware-in-the-loop environments, with emphasis on ML model validation and end-to-end system integrity.