General Motors is seeking to transform transportation through software-driven innovation, focusing on vehicle intelligence and digital engineering. The company aims to integrate Artificial Intelligence and Data Science into critical systems for vehicle design, calibration, and performance, requiring a Staff AI Developer and Data Scientist to architect and deploy scalable AI/ML systems.
Requirements
- 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.
- Experience in automotive or physical systems simulation domains.
Responsibilities
- 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.
- 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).
Other
- Hybrid work arrangement: expected to report to Milford, Michigan three times per week, at minimum.
- Serve as a key technical liaison between simulation teams, software development, platform/cloud architects, HW teams and AI/ML research teams.
- Translate complex engineering needs into actionable AI/ML solutions, balancing innovation with stability and traceability.
- Mentor engineers and data scientists, enabling growth in areas such as model architecture, deployment practices, and responsible AI.
- Establish and champion engineering best practices, coding standards, and documentation norms for AI/ML systems across teams.
