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Plans, executes, and reports autonomous vehicle scenario, regression, integration, and system validation testing. Diagnoses basic software and hardware issues, analyzes vehicle and autonomy data, maintains test documentation and traceability, supports test tools and pipelines, validates software builds against acceptance criteria, and escalates complex defects to engineering teams. The role requires cross-functional collaboration, safety compliance, foundational automotive network knowledge, and 2–4 years of relevant engineering experience.

Leads an engineering team building scalable ML data infrastructure, including data ingestion, processing, storage, access, pipelines, and analytical platforms. Defines architecture, evaluates technologies, improves performance and reliability, translates data requirements into technical solutions, and ensures governance, security, and regulatory compliance. Provides mentorship, strategic roadmap input, and cross-functional collaboration with ML engineers, data scientists, platform engineers, and product teams.

Leads onsite closed-course testing for autonomous vehicles, including test planning, regression and feature validation, fleet readiness, troubleshooting, data capture, issue escalation, and technical reporting. Coordinates limited operations personnel, maintains vehicle configurations and documentation, evaluates test outcomes, supports safety controls, and helps establish a dependable Yuma proving-ground testing operation. The role requires independent work in outdoor conditions, hands-on vehicle activities, and approximately 10% travel.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Architect and build evaluation metrics, statistical and ML analyses, dashboards, and pipelines for autonomous driving systems. Analyze simulation and on-road time-series data, evaluate perception, prediction, and planning models, identify anomalous behavior, and support release-gating and safety decisions. The role requires production Python development, C++ codebase work, cross-team technical leadership, and integration of advanced AI models for scenario classification and validation prioritization.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Lead the architecture and strategy of autonomous vehicle evaluation systems. Develop metrics, statistical and machine-learning methods, automated workflows, dashboards, and release-gating criteria to assess AV performance, identify regressions, and support safety decisions. Collaborate with autonomy, simulation, systems, safety, and data teams across the development lifecycle. Analyze large-scale time-series data, investigate anomalies, influence technical strategy, and provide system-level insights for validated autonomous driving deployment.

Build and operate ETL and data-ingestion pipelines consolidating OT cybersecurity data into Databricks. Develop Python and SQL solutions, data-quality controls, monitoring, and automation; analyze vulnerabilities and cyber risks; and create Power BI dashboards supporting remediation prioritization. Collaborate with cybersecurity, manufacturing, plant, and data engineering teams, document data processes, and improve OT vulnerability-management visibility and reporting.

Lead virtual propulsion analysis and simulation to define system and component requirements for energy consumption, performance, drivability, and regulatory targets. Evaluate ICE, hybrid, and electric propulsion architectures, conduct trade studies, develop analytical methods, coordinate global analysis teams, and present findings to engineering leadership. Collaborate with performance engineers and hardware teams to balance requirements and support next-generation propulsion system development.

Develop, maintain, troubleshoot, and modernize critical mainframe enterprise applications using COBOL, PL/I, JCL, DB2, IMS, and z/OS. Responsibilities span requirements analysis, technical design, coding, testing, deployment, production support, incident resolution, integration, batch processing, security, and modernization. The engineer collaborates with architects and stakeholders, supports cloud integration and CI/CD initiatives, applies Agile and DevOps practices, and mentors less-experienced developers while participating in on-call support.