This role suits an experienced software engineering professional seeking to lead automated verification and test architecture for advanced autonomy platforms in San Diego, California. The Staff Automated Test Engineer position at Shield AI involves driving test infrastructure and MLOps quality systems for unmanned and robotic systems.
Role Overview
The Hivemind Software Engineering Integration and Test team at Shield AI seeks a Staff Automated Test Engineer in San Diego, California. This position focuses on establishing automation standards, defining test architecture, and creating validation infrastructure for next-generation autonomy platforms.
Key Responsibilities
Define the technical strategy and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem.
Build scalable test frameworks for autonomy software, backend services, APIs, and distributed hardware environments.
Execute functional, integration, regression, system, performance, and reliability testing across simulation and hardware-in-the-loop environments.
Construct automated machine learning validation pipelines covering data quality, training reproducibility, accuracy, and resource utilization.
Establish CI/CD and continuous training workflows ensuring versioning and traceability for datasets, models, and deployment artifacts.
Develop scenario-based validation for autonomy models covering edge cases and degraded sensing conditions.
Create observability, analytics, and failure-triage capabilities for software behavior and model drift.
Write Python automation to enhance test execution, parallelization, reporting, and developer productivity.
Collaborate with software, machine learning, and systems engineers to improve designs prior to implementation.
Govern AI-assisted engineering workflows utilizing coding agents and language model tools for test generation and debugging.
Requirements
Typically holds 8 or more years of relevant experience in software engineering, test infrastructure, developer tooling, MLOps, systems integration, or systems verification.
Requires 5 or more years of experience building scalable automation frameworks or developer tooling in Python.
Demonstrated success designing test, CI/CD, or MLOps infrastructure utilized across multiple engineering teams.
Experience validating machine learning systems throughout the data, training, evaluation, packaging, deployment, and monitoring lifecycle.
Understanding of machine learning quality risks such as data leakage, training-serving skew, distribution shift, and model regression.
Experience defining model-performance baselines, automated evaluation suites, and release thresholds.
Experience testing GPU-accelerated infrastructure and workloads within Kubernetes environments.
Experience with performance benchmarking and profiling for GPU workloads to identify bottlenecks.
Experience validating multi-tenant Kubernetes environments including RBAC, resource quotas, and workload isolation.
Experience qualifying integrated hardware and software systems including compute, storage, networking, drivers, and firmware.
Proficiency in asynchronous and concurrent Python programming for scalable automation.
Experience working in Linux-based development environments.
Nice to Have
Experience with model registries, experiment tracking, dataset versioning, and automated artifact promotion using platforms like MLflow, Kubeflow, Weights & Biases, SageMaker, or Vertex AI.
Experience with GPU scheduling and orchestration technologies such as Run:ai, NVIDIA GPU Operator, Kueue, or Volcano.
Familiarity with NVIDIA GPU infrastructure including CUDA, container runtimes, and Multi-Instance GPU.
Experience with GPU profiling tools like NVIDIA Nsight or PyTorch Profiler.
Experience validating perception, planning, reinforcement learning, or autonomy models in simulation and deployed systems.
Experience testing models on embedded or edge-compute platforms regarding latency, memory, power, and quantization.
Skills and Tools
Python
Kubernetes
CI/CD pipelines
MLOps platforms
Linux
GPU profiling tools
About the Company
Shield AI is a venture-backed defense-tech company focused on protecting service members and civilians through intelligent systems. Its product portfolio includes Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation technologies.
How to Apply
Interested candidates should apply directly on the official company careers page.
