3 days ago
Base Salary
$150k - $230k/yr
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, 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 autonomy validation, observability, analytics, failure-triage, test harnesses, simulators, stubs, mocks, and synthetic-data capabilities.
- Build Python automation for test execution, parallelization, reporting, environment setup, experiment comparison, and developer productivity.
- Collaborate with software, autonomy, machine learning, data, simulation, and systems engineers on verification strategies and design improvements.
- Develop and govern AI-assisted engineering workflows using coding agents and LLM-based tools for test generation, log analysis, debugging, and failure triage.
Requirements
- Typically 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.
- 5+ years of experience 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 lifecycles.
- Experience with ML quality risks, 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, resource contention, profiling, and observability in Kubernetes environments.
- Experience validating multi-tenant Kubernetes environments, distributed systems, backend services, APIs, and integrated hardware and software systems.
- Experience qualifying compute, GPU, storage, networking, drivers, firmware, deployed software configurations, dependencies, compatibility, and upgrades.
- Strong system-design skills and understanding of asynchronous and concurrent Python programming for scalable automation and parallel test execution.
- Experience with package and dependency management, reproducible environments, build systems, observability, log collection, analytics, reporting, root-cause analysis, and Linux-based development environments.
- Preferred qualifications include experience with ML platforms, GPU scheduling and orchestration, NVIDIA infrastructure, GPU profiling, autonomy models, embedded or edge-compute platforms, production servers, fault-injection and recovery testing, cloud or air-gapped environments, containers, infrastructure as code, Go or TypeScript, Python interoperability with C or C++, and aerospace, robotics, embedded, or safety-critical software.
- Familiarity with software-in-the-loop, hardware-in-the-loop, requirements-based verification, configuration management, artifact traceability, DO-178C, or MIL-STD-882 is preferred.
Benefits
- Full-time regular employee offer package includes bonus, benefits, and equity in addition to pay within the listed range.
- Temporary employees receive a temporary benefits package applicable after 60 days of employment.
- Offers are contingent on a cleared background and possible reference check.
- Military fellows and part-time employees are not eligible for benefits.
- The posting states that compensation varies by skill set, experience, licenses, certifications, and work location.
Tech Stack
Categories
About Shield AI
Shield AI builds autonomous systems for military and national security customers, combining its Hivemind autonomy software with V-BAT and X-BAT unmanned aircraft and Aechelon simulation technologies. The privately held company sells hardware, software, and related services to U.S. and allied defense agencies. Founded in 2015 and headquartered in San Diego, it operates across the U.S., Europe, the Middle East, and Asia-Pacific, and its technology is used in operational deployments.