Coherent Corp. is seeking to drive semiconductor laser design and manufacturing excellence through the development and deployment of AI/ML within production, focusing on yield improvement, screening accuracy, and design optimization.
Requirements
- Proficient in data pre-processing, feature extraction, dimensionality reduction, clustering, classification, and regression.
- Experience with NN architectures such as CNNs, AEs, GANs and with ensemble learning methods including gradient boosting and random forests is preferred.
- Expertise with ML frameworks: TensorFlow, PyTorch, scikit-learn.
- Proficiency in both Python and C/C++ programming data structures for performance optimization
- Experience in high-performance ML inference (LibTorch, CUDA, ONNX)
- Experience with cloud-based ML platforms (AWS, GCP, Azure) and MLOps platforms such as Kubernetes
- Background in photonics, semiconductor devices, or manufacturing yield optimization is a strong plus
Responsibilities
- Enhance data pipelines across wafer processes, die-level test, and experimentation.
- Develop supervised and unsupervised models for yield prediction and performance screening.
- Apply model selection to identify effective approaches and validate models using known techniques.
- Integrate domain knowledge from semiconductor laser physics into ML models for improved interpretability.
- Document methodologies, model performance, and research findings clearly.
- Develop and validate models based on extensive production data.
- Deploy scalable models through a combination of cloud-based infrastructure and on-premises high performance inference.
Other
- Contract-based position
- On-site
- 6-12 months temporary assignment
- Ability to visualize and communicate insights effectively
- Work closely with photonics researchers and process engineers to align ML approaches with experimental and production objectives and quantify cost-benefit analysis.
