General Motors

Staff Engineer – Virtualization, Knowledge Management, Agentic Simulation

General Motors

full-time

Posted on:

Location Type: Hybrid

Location: Warren • Missouri • United States

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Job Level

Lead

Tech Stack

AirflowDockerGrafanaJenkinsKubernetesNumpyPandasPrometheusPythonPyTorchScikit-LearnTensorflow

About the role

  • 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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
multi-agent architecturesorchestration patternsvalidation frameworksknowledge captureefficiency metricssimulation performance benchmarkingstatistical validationhigh-fidelity control systems simulationpredictive modelingdata analysis
Certifications
PhDMaster’s degreeModeling and Simulation (NTSA)Software quality and testing (ISTQB)QAI's CSTEQAI's CSQASTEC