Staff AI Developer and Machine Learning Engineer

Description

Work Arrangement:

Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to Milford, Michigan three times per week, at minimum.

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.

• 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)

• 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.
• Help define and evolve the technical roadmap for AI/ML within GM's digital twin and simulation ecosystem.

• 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.
• Participate in technical reviews, external publications, or internal tech talks to scale knowledge and influence strategy.

• 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.

• 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.

• Visionary thinking: You identify and pursue novel AI applications in engineering workflows.
• Strategic ownership: You drive initiatives from concept to integration, influencing cross-org direction.
• Cross-domain fluency: You connect simulation, embedded systems, and data science to deliver tangible value.
• Commitment to mentorship: You uplift others and scale your expertise across the team.