AI/ML Senior Researcher

General Motors•Warren, MI

2d•Hybrid

About The Position

The Materials & Manufacturing Systems Research Lab at the General Motors R&D Center is seeking a technically exceptional AI/ML Senior Researcher with expertise in Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), World Models, and Agentic AI for research in Smart Manufacturing Systems. This role involves providing technical and professional leadership to identify, define, and develop future strategic applications of AI in GM’s factory operations. Researchers will counsel engineering management on process improvements, lead GM programs/projects, and represent GM in research activities with external collaborators. The group focuses on developing advanced AI/ML, Agentic AI, mathematical modeling, and optimization approaches to build real-time decision support tools for manufacturing efficiency and quality. The role requires hands-on work to architect, build, deploy, and scale AI/ML models and agentic systems to address complex manufacturing challenges. The ideal candidate is comfortable developing production-grade AI/ML solutions, evaluating emerging technologies, and discussing strategy, roadmap, and business impact with senior leaders. A background in manufacturing systems, smart manufacturing, digital twins, simulation, statistics, quality management, control systems, and machine vision is desirable, with experience in production operations being a plus.

Requirements

PhD or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Engineering, Physics, or a related field, with significant relevant experience.
Demonstrated success building and deploying AI/ML models in production or high-value applied research environments.
Deep expertise across multiple AI domains, including machine learning, deep learning, LLMs, generative AI, world models, and physics-based machine learning.
Strong understanding of modern AI/ML development workflows, including data pipelines, model training, evaluation, deployment, monitoring, and lifecycle management.
Experience applying AI/ML to complex technical domains such as engineering, simulation, scientific computing, and advanced manufacturing engineering.
Background in manufacturing process modeling, design, control, and optimization.
Manufacturing engineering theory and principles of production management & operations.
Excellent collaborative skills.
Proven record of high impact publications in peer-reviewed conferences/journals.

Nice To Haves

PhD in relevant field of study
0-6 years of research experience beyond PhD. (university, research institution, or industrial research)
Background in manufacturing process modeling, design, control, and optimization with track record of successful manufacturing implementations
Proficient in data manipulation, analysis, and visualization
Structured database design and integration experience along with programming skills (e.g. SQL, C++, Python, R, Matlab)
Familiarity with AI tools: Scikit Learn, Tensorflow, Keras, PyTorch

Responsibilities

Build, validate, deploy, and scale AI/ML models for high-impact manufacturing engineering, research and development applications.
Lead technical work across a broad set of AI domains, including classical machine learning, deep learning, GenAI, Large Language Models, multimodal AI, agentic AI, World Models, and physics-based AI.
Translate business and engineering problems into practical AI/ML solution strategies and executable technical plans.
Serve as a technical authority on model development, deployment architectures, data strategy, evaluation methods, and responsible AI practices.
Generate new concepts and ideas while conducting research programs that apply the latest AI/ML tools.
Drive technical assignments in a resourceful and timely manner with minimal supervision.
Work well with fellow researchers, centers, operational groups, plant personnel and universities.
Maintain state-of-art technical skills and knowledge.
Develop working relationships with internal and external subject matter experts.
Effectively communicate and detail results through internal and external publication.
Collaborate with global technical teams and lead discussions as an expert in manufacturing AI/ML.