At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.
Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.
The Role
Lead the development of agentic simulation frameworks, AI-driven knowledge management systems, and virtualization strategies for complex engineering environments. This role emphasizes performance validation methodologies , predictive analytics for AI tool efficacy, and advanced simulation orchestration.
What You’ll Do
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.
AI Knowledge Management
- 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.
Virtualization & Simulation Performance
- 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.
Validation Methodologies
- Establish quantitative metrics for simulation fidelity and decision-making efficiency.
- Implement predictive analytics for AI deployment success using A/B testing, offline replay, and sensitivity analysis.
- Document validation evidence for enterprise-scale rollouts.
Your Skills & Abilities (Required Qualifications)
Education
- 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.
Agentic Simulation
- Experience designing multi-agent architectures and orchestration patterns.
- Experience with agent-based modeling frameworks and collective intelligence optimization.
AI Knowledge Management
- Expertise in validation frameworks, knowledge capture, and efficiency metrics.
- Ability to predict and validate AI tool efficacy using simulation and statistical methods.
Performance Validation
- 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.
Performance Analysis Tools
- 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.
What Will Give You a Competitive Edge (Preferred Qualifications)
- Demonstrated success in deploying AI-driven KM systems with measurable efficiency gains.
- Experience validating simulation frameworks under stress and scalability scenarios.
- Proven ability to forecast AI tool performance using predictive analytics and simulation-based testing.
