Principal Machine Learning Engineer

at  Corning

Date: Jul 29, 2024
Location: Charlotte, NC, US, 28216
Company: Corning
Requisition Number: 64133
Corning is vital to progress – in the industries we help shape and in the world we share.
We invent life-changing technologies using materials science. Our scientific and manufacturing expertise, boundless curiosity, and commitment to purposeful invention place us at the center of the way the world interacts, works, learns, and lives.
Our sustained investment in research, development, and invention means we’re always ready to solve the toughest challenges alongside our customers.
The global Information Technology (IT) Function is leading efforts to align IT and Business Strategy, leverage IT investments, and optimize end to end business processes and associated information integration technologies. Through these efforts, IT helps to improve the competitive position of Corning’s businesses through IT enabled processes. IT also delivers Information Technology applications, infrastructure, and project services in a cost efficient manner to Corning worldwide.

EDUCATION REQUIREMENT:

• Advanced Degree: A master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is preferred.

REQUIRED SKILLS:

• Knowledge of Data Science and Machine Learning concepts and algorithms such as clustering, regression, classification, forecasting, hyperparameters optimization, NLP, computer vision, speech processing
• Programming Skills: Proficiency in AI/ML programming languages such as Python or R. Experience with libraries like TensorFlow, PyTorch, or Keras is essential.
• Deep Learning Knowledge: Thorough understanding of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures.
• Familiarization with Generative Artificial Intelligence (GenAI), such as Large Language Models (LLMs), LangChain, HuggingFace, Retrieval Augmented Generation, etc.
• Understanding of ML model lifecycle, experience working with MLflow and a demonstrated ability to lead architecture efforts for its implementation.

DESIRED SKILLS:

• Strong leadership and excellent verbal and written communications skills, with the ability to develop and sell ideas.
• Research Skills: Ability to stay updated with the latest research papers and industry trends in generative AI (Artificial Intelligence).
• Cultural bias towards continual learning, sharing best practice, encouraging, and elevating less experienced colleagues as they learn.
• Industry Knowledge: Familiarity with manufacturing processes, industry standards, and relevant regulations. A degree or background in Industrial Engineering, Manufacturing Engineering, or related field can be helpful.

As an ML Engineer, IT Manufacturing, your main responsibilities will be:

• Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence to solve business problems.
• Designing and implementing portable, modular, instrumented and highly performant data contextualization pipelines from landed and cleansed, batch and streamed unstructured data, using Apache Spark, Delta Lake and/or Databricks.
• Designing and implementing portable, modular, instrumented and highly performant model deployment pipelines for many types of machine learning including supervised and unsupervised learning as well as CNNs, RNNs or other deep learning algorithms.
• Working closely with domain expert data scientists, process, and controls engineers, both within and outside the company to understand and automate transformation, normalization and other contextualization operations based on the types of analytics being performed on the inbound datasets, as well as model performance management requirements and design suitable inferencing instrumentation systems and practices that meet them.
• Working with your fellow developers using agile development practices, and continually improving development methods with the goal of automating the build, integration, deployment and monitoring of production inferencing and dataset delivery systems.