Corning is looking to transform its global manufacturing operations by implementing production-grade AI/ML solutions, requiring the development and scaling of models and data pipelines.
Requirements
- Strong Python (or R) and experience with scikit‑learn, TensorFlow, PyTorch, Keras; Docker for containerization.
- Deep learning fundamentals (CNNs, RNNs) plus ML concepts (clustering, regression, classification, forecasting).
- Familiarity with GenAI/LLMs (LangChain, Hugging Face, RAG) and NLP techniques (tokenization/vectorization, semantic search, clustering).
- Robust software engineering mindset, CI/CD, monitoring, and model performance management.
- Experience in manufacturing environments and controls/industrial systems.
- Leading ML architecture and model registry implementations.
- Databricks or other cloud analytics platforms.
Responsibilities
- Design and develop ML systems, training and optimizing models, and deploying and maintaining them in production environments
- Build modular, high‑performance model pipelines for traditional ML and deep learning (random forests, CNNs, RNNs).
- Develop reliable data pipelines to ingest, clean, and transform structured and unstructured data from APIs, databases, files, and streams.
- Perform EDA, feature engineering, and data validation to improve model outcomes.
- Translate business needs into technical solutions; document, review code, and contribute in Agile sprints.
- Own the ML lifecycle using registries (e.g., MLflow) and MLOps best practices.
- Work across AWS services (SageMaker, ECS, S3, Glue, Greengrass); Databricks experience is a plus.
Other
- BS/MS in Computer Science, Mathematics, Engineering, or related field.
- 5+ years building and deploying ML in production.
- Clear communication and collaborative working style.
- Comfort in highly matrixed organizations; proactive, growth mindset.
- This position does not support immigration sponsorship.
