Hybrid Staff Engineer – Virtualization, Knowledge Management, Agentic Simulation

at General Motors

• Job title: Staff Engineer – Virtualization, Knowledge Management, Agentic Simulation
• Job type: Full Time
• Experience level: Lead
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: Airflow, Docker, Grafana, Jenkins, Kubernetes, Numpy, Pandas, Prometheus, Python, PyTorch, Scikit-Learn, Tensorflow
• Location requirements: Hybrid, Warren, United States

About the role

• Lead development of simulation frameworks and AI-driven knowledge management systems for General Motors. Architect multi-agent workflows and validate performance metrics in complex engineering environments.

Responsibilities

• Lead the development of agentic simulation frameworks, AI-driven knowledge management systems, and virtualization strategies for complex engineering environments.
• Agentic Simulation Frameworks: Architect multi-agent workflows for design, analysis, and decision-support.
• Implement orchestration strategies for parallel and serial agent execution.
• Integrate AI agents for requirement verification, optimization, and adaptive learning.
• Deploy Validation strategies for authenticating, synchronization and optimizing organizational knowledge.
• Design Knowledge Management (KM) pipelines across multiple platforms and develop predictive models for AI tool efficacy.
• Validate KM systems through large-scale multi-agent simulations.
• Develop virtualization strategies for scalable simulation environments.
• Conduct performance analysis: throughput, latency, determinism, resource utilization, and robustness under stress.
• Apply statistical validation frameworks to ensure reproducibility and confidence in simulation results.
• Establish quantitative metrics for simulation fidelity and decision-making efficiency.

Requirements

• PhD or Master’s degree in Electric Engineering, Computer Engineering, or related field.
• Professional education in Modeling and Simulation (NTSA or alike)
• Professional education in Software quality and testing (ISTQB, QAI's CSTE/CSQA or STEC)
• 10+ years of experience delivering embedded or system-level software in production environments.
• Experience designing multi-agent architectures and orchestration patterns.
• Expertise in validation frameworks, knowledge capture, and efficiency metrics.
• Strong background in simulation performance benchmarking: latency, determinism, scalability, and robustness.
• Proficiency in statistical validation (confidence intervals, effect sizes, hypothesis testing).
• Strong background with high-performance high-fidelity control systems simulation for Electric Drive, Power Electronics and RESS Software & Tools Programming: MATLAB/Simulink, Python, C++ for simulation control, data analysis, and ML integration.
• AI/ML: PyTorch, TensorFlow, scikit-learn for predictive modeling.
• Data Analysis: NumPy, Pandas, SciPy, visualization with Matplotlib, Seaborn.
• Experiment Tracking: MLflow, Airflow, Prefect.
• CI/CD: Git, GitLab, Jenkins, containerization with Docker/Kubernetes.
• Profilers: Perf, VTune, Nsight Systems for CPU/GPU utilization.
• Timing & Jitter Analysis: Chrony, Perfetto, Trace Compass.
• Resource Monitoring: Prometheus/Grafana, htop, nmon.
• Statistical Analysis: R, SciPy, Statsmodels for hypothesis testing and confidence metrics.

Benefits

• From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions.