Sr. Engineer, Machine Learning

Role Summary

Corning is seeking a Sr. Engineer, Machine Learning to join a growing Manufacturing Technology and Engineering team in Durham, North Carolina. This person will help develop and deploy applied machine learning, AI, and analytics solutions that address real manufacturing and operational challenges across Corning.The role will work with technical customers to understand the underlying business or engineering problem, develop practical modeling approaches, and help transition solutions into production environments. Projects may include predictive maintenance, computer vision and inspection systems, process modeling, and LLM-integrated applications such as MCP- or RAG-based assistants.The team is looking for a strong applied machine learning foundation rather than prior manufacturing experience specifically; manufacturing, equipment, or industrial-data experience is beneficial but not required.

Key Responsibilities

• Partner with manufacturing, engineering, and business stakeholders to understand technical problems and develop machine learning or AI solutions that deliver practical business impact

• Perform exploratory and targeted data analysis; prepare data, select appropriate methods, develop models, evaluate results, and clearly communicate recommendations

• Develop machine learning solutions for classification, regression, clustering, predictive maintenance, condition-based maintenance, process modeling, computer vision, or inspection-system applications

• Develop and support LLM-integrated applications, including MCP- and RAG-based process, equipment, or knowledge assistants

• Build predictive and classification models and support deployment into production environments, including on-premise systems, servers, cloud, or edge devices

• Configure, manage, and troubleshoot machine learning application environments, data workflows, and deployment infrastructure as needed

• Document technical approaches, model development, validation, results, and recommendations through reports and presentations

• Collaborate across multidisciplinary teams and contribute to identification, scoping, and development of new machine learning opportunities

Experiences/Education - Required

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related technical discipline with two or more years of relevant applied experience; or a master’s or doctoral degree with substantial applied project, capstone, thesis, internship, or research experience

• Applied experience using machine learning methods for classification, regression, clustering, forecasting, or related analytical problems

• Strong foundation in statistics, data science, model evaluation, and data-driven problem solving

• Experience in at least one area of machine learning, deep learning, computer vision, or large language models

• Proficiency in Python and working knowledge of object-oriented programming

• Experience with at least one major machine learning framework or data-analysis library, such as PyTorch, TensorFlow, Pandas, Polars, DuckDB, or PySpark

• Familiarity with Git and version-control practices

• Ability to understand an engineering or business problem, select appropriate analytical methods, and explain the rationale behind the approach

• Ability to communicate technical findings effectively to both technical and nontechnical stakeholders

• Ability to work in the United States without current or future immigration sponsorship and meet export-control eligibility requirements

Experiences/Education - Desired

• Experience with predictive maintenance, condition-based maintenance, manufacturing analytics, equipment data, process modeling, or industrial-data applications

• Experience with computer vision, inspection systems, OpenCV, HALCON, convolutional neural networks, recurrent neural networks, YOLO, or related techniques

• Familiarity with LLM application development, Claude Code, OpenCode, Codex, Model Context Protocol, retrieval-augmented generation, agentic AI frameworks, or related tools

• Experience with Linux, CUDA, GPU computing, NVIDIA edge-device environments, cloud platforms, or production machine learning deployment

• Experience with MATLAB, C#, R, LabVIEW, C++, C, SQL, ETL workflows, data extraction, data cleansing, or data manipulation

• Experience with Streamlit, Dash, Flask, Blazor, Power BI, or other application-development, visualization, or reporting tools

• Familiarity with CI/CD pipelines and cloud-based storage or compute environments such as AWS or Microsoft Azure

• Experience transitioning proof-of-concept machine learning work into deployed production applications

• Experience identifying machine learning opportunities, shaping technical proposals, or building stakeholder support for data-driven solutions