WHY JOIN US

General Motors is at the forefront of transforming transportation through software-driven innovation. We’re driven by our bold vision of a future with Zero Crashes, Zero Emissions, and Zero Congestion. As we push forward into an era of vehicle intelligence and digital engineering, Artificial Intelligence and Data Science are a cornerstone of our strategy.
You’ll be part of a team that is pioneering the integration of simulation, automation, AI agents, large language models (LLMs), and machine learning into critical systems for vehicle design, calibration, and performance. We’re seeking a Staff AI Developer and Data Scientist to serve as a key technical expert, shaping the evolution of our tools and infrastructure while delivering scalable, intelligent solutions that drive real-world engineering impact.

YOUR SKILLS & ABILITIES (REQUIRED QUALIFICATIONS):

• Bachelor’s in Computer Science, Engineering, Mathematics, or related field (particularly in NLP, simulation AI, or reinforcement learning).
• 7+ years of experience building and deploying advanced machine learning or deep learning systems in production.
• Demonstrated expertise with LLMs, transformer architectures, AI agents, or simulation-integrated models.
• Strong experience in Python, major ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace Transformers), SQL, and signal processing libraries (PyWavelets, Tsfresh).
• Experience with retrieval-augmented generation (RAG), prompt engineering, and embedding optimization.
• Knowledge of ML modeling and toolsets (e.g. Scikit-learn, XGBoost for classification/regression tasks)
• Experience with MLOps tools and deploying models via containerized microservices on cloud platforms.
• Proven ability to lead technical direction and deliver production-ready AI/ML systems at scale.
• Strong interpersonal and communication skills and a willingness to collaborate cross-functionally with different teams.

WHAT CAN GIVE YOU A COMPETITIVE ADVANTAGE (PREFERRED QUALIFICATIONS):

• Master’s or PhD in Computer Science, Engineering, Mathematics, or related field (particularly in NLP, simulation AI, or reinforcement learning).
• 7+ years of experience building and deploying advanced machine learning or deep learning systems in production.
• Experience in automotive or physical systems simulation domains.
• Familiarity with co-simulation frameworks, physical modeling (e.g., Simulink, Modelica), or system-level calibration workflows.
• Knowledge of optimization techniques such as PSO, GA, or MDO in the context of AI/simulation fusion.
• Contributions to open-source AI tools or published research in NLP, agents, or simulation-integrated AI.

THE ROLE:

As a Staff AI Developer and Data Scientist, you will operate as a senior technical expert and strategic contributor within a growing AI/ML-focused engineering team. You will architect and deploy scalable AI/ML systems (e.g., LLMs, AI agents, retrieval-augmented generation (RAG), and hybrid AI-simulation models) that enable transformative use cases across simulation, calibration, and product development.
You will work cross-functionally with engineers, data scientists, simulation specialists, domain experts and platform teams to define and execute high-impact AI/ML initiatives. Your role will blend hands-on development, technical direction-setting, and mentorship, helping GM scale next-generation capabilities.

WHAT YOU’LL DO:

• Prototype, and productionize scalable AI systems, with an emphasis on LLMs, simulation-aware models, and hybrid AI pipelines.
• Lead AI/ML integration into core engineering tools and simulation frameworks, ensuring robustness, interpretability, and physical relevance of outputs.
• Evaluate and define the appropriate use of RAG systems, fine-tuning vs. zero/few-shot learning strategies, and feedback loops for continuous improvement.
• Drive forward-thinking initiatives involving multi-agent AI systems, context-aware simulation orchestration, or generative design techniques.
• Develop custom feature extraction methods for predictive modeling then used in optimizations.
• Apply statistical methods, anomaly detection, and clustering to uncover patterns.
• Work with large scale data sets and collaborate with subject matter experts to incorporate physical interpretations of insights
• Create interactive data visualizations to communicate and interpret
• Design and build ML models that may be used as surrogates in simulations
• Develop and operationalize full-stack AI pipelines using MLOps practices (e.g., Docker, Kubernetes, FastAPI, MLFlow, cloud-native services).
• Define strategies for large-scale data ingestion, embedding generation, retrieval tuning, and prompt optimization in production environments.
• Ensure scalability, reproducibility, and performance of deployed models through well-defined evaluation, monitoring, and retraining mechanisms.
• Use data analytics and signal processing to analyze simulation output data using techniques like wavelet transforms and motif discovery (or other)