Sr. Engineer, Machine Learning
- Trusted products that accelerate drug discovery, development, and delivery to save lives
- Damage-resistant cover glass to enhance the devices that keep us connected
- Optical fiber, wireless technologies, and connectivity solutions to carry information and ideas at the speed of light
- Precision glass for advanced displays to deliver richer experiences
- Auto glass and ceramics to drive cleaner, safer, and smarter transportation
- 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.
- Travel requirements will vary. Expected travel up to 25% both Domestic and International
- Bachelors 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, Principal Components Analysis, 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: TensorFlow, PyTorch, Streamlit, Dash, Pandas, Polars, PySpark
- Demonstrated experience with at least two visualization tools, e.g. , Streamlit, Dash, Flask, Power BI, Blazor, etc.
- 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 businesses.
- Ability to identify ML opportunities, build proposals, and gain stakeholder buy-in.
- Work with senior engineers to grow the overall analytics capabilities of the team.
- Strong attention to detail and organizational skills.
- Ability to work collaboratively in a team environment across stages of innovations.
- Ability to communicate the outcome of complex and/or technical concepts in a clear manner to a non-analytical group.
- Ability to transition a proof-of-concept into deployed production applications.
- Software development experience in C#, C++, or C
- Experience of setting up CUDA environment on Nvidia edge devices
- Familiarity with CI/CD pipelines
- Fluency with Linux and Windows commands-line tools
- Familiarity with OpenCV
- Knowledge of HALCON for Computer Vision is a plus
- Software version control experience (i.e. Git)
- Familiarity with basic concepts of Large Language Models (LLM)
- Familiarity with ETL tools for data extraction, data cleansing, and data manipulation.
- Fluency with SQL
- In-depth knowledge of Deep Learning techniques (e.g., CNN, RNN, YOLO) and proper use of libraries such as PyTorch and TensorFlow
- Familiarity with cloud-based storage and compute environments (AWS, MS Azure)
- Company-wide bonuses and long-term incentives align with key business results and ensure you are rewarded when the company performs well. When Corning wins, we all win.
- As part of our commitment to your financial well-being, we provide a 100% company-paid pension benefit with fixed contributions that grow throughout your career. Combined with matching contributions to your 401(k) savings plan, Corning’s total contributions to your retirement accounts can reach between 7% and 12% of your pay, depending on your age and years of service.
- Our health and well-being benefits include medical, dental, vision, paid parental leave, family building support, fitness, company-paid life insurance, disability, disease management programs, paid time off, and an Employee Assistance Program (EAP) to support you and your family.
- Getting paid for our work is important, but feeling appreciated and recognized for those contributions motivates us much more. That’s why Corning offers a recognition program to celebrate successes and reward colleagues who make exceptional contributions.
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