Sr. Engineer, Machine Learning

Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space, our technologies push the boundaries of what’s possible.

The company built on breakthroughs.
Join us.

Do you want to be part of a unique opportunity to join a growing team that is demonstrating value and driving business impact within Corning Incorporated? We are increasing our application of advanced analytics and expert systems to our growing manufacturing capabilities. New manufacturing measurements and controls systems provide vast amounts of untapped process and product data. Your responsibilities will include transforming our data assets into strong internal insights that drive strategic solutions to deliver large value impact.

You will act as a key member of multi-disciplined teams solving diverse challenges as you work across the many businesses that comprise Corning.

• Successfully identify the real problem from the customer’s point of view to build and execute predictive maintenance, computer vision, inspection systems, or process modeling work plans that deliver business impact.
• Communicate technical analyses and results to division and manufacturing plant customers.
• Perform exploratory and targeted data analysis using commercial and open-source data analytics software packages.
• Formulate algorithms to develop predictive maintenance or condition-based maintenance strategies for manufacturing equipment. Perform systems design to deploy the developed solutions in production.
• Develop computer vision and deep learning based solutions to solve problems for various areas of manufacturing such as inspection systems, process workflow, etc.
• Apply multivariate statistical methods and machine learning techniques to find structure and value in both large and small data sets to identify key process input variables that can feed process control solutions.
• Develop predictive/classification models for use in production. Write technical reports summarizing development, application, and validation of the technical analysis.
• Configure/manage deployment environments for machine learning applications including but not limited to on-premise workstations/servers, cloud, or edge devices.

• Expected travel up to 25% both domestic and international.

• Bachelor’s degree with 2 years’ experience or Master’s degree or PhD degree with capstone/thesis experience in Computer Science or an Engineering discipline. Certification desired in one of Deep Learning, Convolutional Neural Networks, CUDA-based acceleration for ML, or Deep Learning with Computer Vision.

• Basic experience with object-oriented programming.
• Passion, knowledge of, and experience with applying ML/statistical algorithms for classification, regression, and clustering, e.g., CNNs, Transformers, Random Forest, SVM, Logistic Regression, clustering, Bayesian models, PCA, Partial Least Squares, etc. The ideal candidate should be able to explain their reasoning of employing a certain algorithm.
• Strong knowledge of statistics and data science.
• Familiarity with basics of optimization theory.
• Experience in at least one area among machine learning, deep learning, computer vision.
• Familiarity with basic Linux commands.
• Familiarity with basics of CUDA and GPU.
• Technical experience with at least two of the following languages/tools:
  Python, Matlab, C#, R, LabVIEW, C++, JMP.
• Experience with at least three of the following framework/libraries:
  Tensor Flow, PyTorch, Streamlit, Dash, Pandas, Polars, PySpark.
• Demonstrated experience with at least two visualization tools, e.g., Streamlit, Dash, Flask, Power BI, Blazor.
• Ability to understand the underlying engineering principle is a must. Blind fitting of machine learning technique to a data set is to be avoided.
• Ability to show the relevance of the applied techniques in machine learning, and prioritizing work across multiple projects will be critical.
• Ability to serve as an analytics translator for multiple business groups by understanding business needs and determining how analytics can help…