{"id":1321381,"url":"https://alion.io/job/shield-ai-staff-engineer-test-automation-r5792","title":"Staff Engineer, Test Automation (R5792)","company":{"id":50,"name":"Shield AI","domain":"shield.ai","url":"https://alion.io/company/shield-ai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":74,"open_postings":84,"ghost_share":0,"stale_share":0.845,"repost_share":0.012,"time_to_fill_p50_days":65,"computed_at":"2026-11-01T06:10:25Z"}},"role":"QA","role_family":"QA","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Diego, United States","San Mateo, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":157000,"max":235800,"currency":"USD","period":"year","gross":null,"usd_annual":235800},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Conan","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Poetry","optional":false},{"name":"Python","optional":false},{"name":"Setuptools","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"C++","optional":true},{"name":"CUDA","optional":true},{"name":"Cython","optional":true},{"name":"DO-178C","optional":true},{"name":"Go","optional":true},{"name":"Kubeflow","optional":true},{"name":"MIL-STD-882","optional":true},{"name":"MLFlow","optional":true},{"name":"PyTorch","optional":true},{"name":"Quantization","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"TypeScript","optional":true},{"name":"Vertex AI","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-09-26T20:48:27Z","employer_posted_date":"2026-09-26","last_verified_at":"2026-11-01T08:32:34Z","board_verified":true,"closed_at":null,"days_open":35,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":35},"description":"Job Description:\nShield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.\nThe Hivemind Software Engineering Integration and Test team is seeking a Staff Automated Test Engineer to provide technical leadership for automated verification across our next-generation autonomy platform. You will define and evolve test architecture, validation infrastructure, MLOps quality systems, and CI/CD pipelines that enable the Hivemind software ecosystem to create, test, and deploy resilient autonomy capabilities for unmanned aircraft and robotic platforms operating in complex, contested, and GPS-denied environments.\nYou will work across production flight code, machine learning models, simulation and synthetic environments, mission planning and orchestration systems, operator-facing Ground Control Station applications, telemetry and data pipelines, cloud-native developer infrastructure, and hardware-in-the-loop systems.\nIn this hands-on Staff role, you will lead complex, cross-functional initiatives, establish automation and verification standards, identify systemic quality risks, and develop scalable test infrastructure. The ideal candidate combines deep Python automation and distributed-systems testing experience with technical leadership and experience validating machine learning systems throughout the data, training, evaluation, deployment, and monitoring lifecycle.\nWhat you'll do:\nOwn the technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem.\nDesign and maintain scalable test frameworks for autonomy software, backend services, APIs, operator-facing applications, and distributed hardware environments.\nLead 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.\nBuild automated ML validation pipelines covering data quality, training reproducibility, model accuracy, robustness, regression, latency, resource utilization, and system integration.\nEstablish CI/CD and continuous training workflows that provide versioning and traceability for datasets, models, configurations, evaluation results, and deployment artifacts.\nDevelop scenario-based validation for autonomy models, including edge cases, degraded sensing or communications, distribution shifts, and representative mission conditions.\nCreate observability, analytics, and failure-triage capabilities for software behavior, model and data drift, inference health, test results, and production performance.\nBuild Python automation that improves test execution, parallelization, reporting, environment setup, experiment comparison, and developer productivity.\nCreate test harnesses, simulators, stubs, mocks, and synthetic data capabilities that improve system testability and coverage.\nCollaborate with software, autonomy, machine learning, data, simulation, and systems engineers to define verification strategies and improve designs before implementation.\nDevelop and govern AI-assisted engineering workflows using coding agents and LLM-based tools for test generation, log analysis, debugging, and failure triage while maintaining security, reproducibility, and traceability.\nRequired qualifications:\nTypically 8+ years of relevant experience in software engineering, test infrastructure, developer tooling, MLOps, systems integration, or systems verification, or an equivalent combination of experience and demonstrated impact.\n5+ years of experience building scalable automation frameworks or developer tooling in Python.\nDemonstrated success designing test, CI/CD, or MLOps infrastructure used across multiple engineering teams.\nExperience validating machine learning systems across the data, training, evaluation, packaging, deployment, and monitoring lifecycle.\nUnderstanding of ML quality risks such as data leakage, training-serving skew, nondeterminism, distribution shift, drift, model regression, and statistical acceptance criteria.\nExperience defining model-performance baselines, automated evaluation suites, release thresholds, and candidate-to-production comparison workflows.\nExperience testing GPU-accelerated infrastructure and workloads, including GPU scheduling, allocation, utilization, and resource contention in Kubernetes environments.\nExperience with performance benchmarking, profiling, and observability for GPU workloads, including identifying compute, memory, storage, networking, and data-loading bottlenecks.\nExperience validating multi-tenant Kubernetes environments, including RBAC, resource quotas, workload isolation, and scheduling behavior.\nExperience qualifying integrated hardware and software systems, including automated validation of compute, GPU, storage, networking, drivers, firmware, and deployed software configurations.\nStrong system-design skills and experience testing distributed systems, backend services, APIs, and integrated hardware and software environments.\nExperience developing integration and regression strategies for internally developed, third-party, open-source, and partner software, including dependency management, compatibility testing, and upgrades.\nStrong understanding of asynchronous and concurrent Python programming for scalable automation and parallel test execution.\nExperience with package and dependency management, reproducible environments, and build systems such as Conan, pip, setuptools, Poetry, Nix, or similar.\nExperience with automated observability, log collection, analytics, reporting, and root-cause analysis in complex software, data, and infrastructure systems.\nExperience working in Linux-based development environments.\nPreferred qualifications:\nExperience with model registries, experiment tracking, dataset or feature versioning, model serving, and automated artifact promotion using MLflow, Kubeflow, Weights & Biases, SageMaker, Vertex AI, or similar platforms.\nExperience with GPU scheduling and orchestration platforms such as Run:ai, NVIDIA GPU Operator, KAI Scheduler, Kueue, Volcano, or similar technologies.\nExperience with NVIDIA GPU infrastructure, including CUDA, drivers, container runtimes, Multi-Instance GPU, GPU fractionalization, and hardware/software compatibility testing.\nExperience with GPU profiling and performance-analysis tools such as NVIDIA Nsight, PyTorch Profiler, or similar technologies.\nExperience validating perception, planning, decision-making, reinforcement learning, or other autonomy models in simulation and on deployed systems.\nExperience testing models on embedded or edge-compute platforms, including latency, memory, power, accelerator compatibility, quantization, and hardware-specific behavior.\nExperience qualifying production servers or appliances, including hardware validation, burn-in, provisioning, firmware, networking, storage, and software-stack validation before deployment.\nExperience with reliability, fault-injection, and recovery testing across distributed compute, storage, networking, and GPU infrastructure.\nExperience validating reproducible installation, operation, upgrades, and rollback in cloud, on-premises, disconnected, or air-gapped environments.\nExperience with containers, Kubernetes, cloud infrastructure, infrastructure as code, and reproducible test environments.\nProficiency with Go or TypeScript for automation tooling or UI test development.\nExperience integrating Python with native C or C++ applications through bindings, wrappers, subprocess interfaces, or similar interoperability tooling.\nAerospace, robotics, autonomy, embedded systems, or safety-critical software experience.\nFamiliarity with software-in-the-loop, hardware-in-the-loop, requirements-based verification, configuration management, artifact traceability, or standards such as DO-178C and 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