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Shield AI is seeking a Staff Automated Test Engineer to provide technical leadership for automated verification across its next-generation Hivemind autonomy platform. This hands-on Staff role owns the technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem—which includes autonomy software, unmanned aircraft (V-BAT, X-BAT), simulation and synthetic reality technologies, and robotic platforms operating in complex, contested, and GPS-denied environments.
You will design and maintain scalable test frameworks spanning production flight code, machine learning models, simulation and synthetic environments, mission planning systems, Ground Control Station applications, telemetry pipelines, cloud-native infrastructure, and hardware-in-the-loop systems. Key responsibilities include:
- Own technical strategy and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem
- Design scalable test frameworks for autonomy software, backend services, APIs, operator-facing applications, and distributed hardware environments
- Lead functional, integration, regression, system, performance, reliability, and end-to-end testing across simulation, edge-compute, software-in-the-loop, and hardware-in-the-loop environments
- Build automated ML validation pipelines covering data quality, training reproducibility, model accuracy, robustness, regression, latency, resource utilization, and system integration
- Establish CI/CD and continuous training workflows with versioning and traceability for datasets, models, configurations, evaluation results, and deployment artifacts
- Develop scenario-based validation for autonomy models, including edge cases, degraded sensing/communications, distribution shifts, and representative mission conditions
- Create observability, analytics, and failure-triage capabilities for software behavior, model and data drift, inference health, test results, and production performance
- Build Python automation for test execution, parallelization, reporting, environment setup, experiment comparison, and developer productivity
- Create test harnesses, simulators, stubs, mocks, and synthetic data capabilities
- Collaborate with software, autonomy, ML, data, simulation, and systems engineers to define verification strategies
- Develop and govern AI-assisted engineering workflows using coding agents and LLM-based tools for test generation, log analysis, debugging, and failure triage
The ideal candidate combines deep Python automation and distributed-systems testing expertise with technical leadership experience and proven ability to validate machine learning systems throughout the data, training, evaluation, deployment, and monitoring lifecycle.
**Requirements:**
- Typically 8+ years of relevant experience in software engineering, test infrastructure, developer tooling, MLOps, systems integration, or systems verification (or equivalent combination of experience and demonstrated impact)
- 5+ years building scalable automation frameworks or developer tooling in Python
- Demonstrated success designing test, CI/CD, or MLOps infrastructure used across multiple engineering teams
- Experience validating machine learning systems across data, training, evaluation, packaging, deployment, and monitoring lifecycle
- Understanding of ML quality risks: data leakage, training-serving skew, nondeterminism, distribution shift, drift, model regression, and statistical acceptance criteria
- Experience defining model-performance baselines, automated evaluation suites, release thresholds, and candidate-to-production comparison workflows
- Experience testing GPU-accelerated infrastructure and workloads, including GPU scheduling, allocation, utilization, and resource contention in Kubernetes environments
- Experience with performance benchmarking, profiling, and observability for GPU workloads (compute, memory, storage, networking, data-loading bottlenecks)
- Experience validating multi-tenant Kubernetes environments: RBAC, resource quotas, workload isolation, scheduling behavior
- Experience qualifying integrated hardware and software systems, including automated validation of compute, GPU, storage, networking, drivers, firmware, and deployed software configurations
- Strong system-design skills and experience testing distributed systems, backend services, APIs, and integrated hardware/software environments
- Experience developing integration and regression strategies for internally developed, third-party, open-source, and partner software
- Strong understanding of asynchronous and concurrent Python programming for scalable automation and parallel test execution
- Experience with package and dependency management, reproducible environments, and build systems (Conan, pip, setuptools, Poetry, Nix, or similar)
- Experience with automated observability, log collection, analytics, reporting, and root-cause analysis in complex software, data, and infrastructure systems
- Experience working in Linux-based development environments
**Preferred qualifications include:** model registries and experiment tracking (MLflow, Kubeflow, Weights & Biases, SageMaker, Vertex AI), GPU scheduling platforms (Run:ai, NVIDIA GPU Operator, KAI Scheduler, Kueue, Volcano), NVIDIA GPU infrastructure (CUDA, drivers, container runtimes, Multi-Instance GPU), GPU profiling tools (NVIDIA Nsight, PyTorch Profiler), autonomy model validation (perception, planning, decision-making, reinforcement learning), edge-compute testing, production server qualification, reliability and fault-injection testing, reproducible installation and rollback validation, containers and Kubernetes, Go or TypeScript proficiency, Python/C/C++ interoperability, aerospace/robotics/autonomy/embedded systems/safety-critical software experience, and familiarity with software-in-the-loop, hardware-in-the-loop, requirements-based verification, configuration management, and standards such as DO-178C and MIL-STD-882.
