Job description

Primary Duties & Responsibilities • Develop and validate models for yield improvement, screening accuracy, and process optimization through application of model selection and hyperparameter tuning. • Collaborate with Photonics designers and process, reliability and manufacturing engineers to align ML approaches with product objectives and to quantify cost-benefit analysis. • Deploy AI/ML within manufacturing systems through a combination of Edge AI, API serving, Containerization, and Cloud-based training and inference • Partner with industrial and MES software engineers to integrate AI/ML pipelines within existing production workflows • Develop reusable data pipelines, analytical tools, dashboards, and model-monitoring methods. • Support design of experiments, process characterization, and continuous improvement activities. • Help establish best practices for ML, and share the practices across the site and other sites. Skills The Candidate will have competency in the following areas: • Expertise in deep learning frameworks such as Pytorch, TensorFlow • Expertise deep learning architectures such as CNNs, RNNs, GANs • Expertise in ML methods such as Random Forests and Gradient Boosting • Experience with clustering, feature engineering, and dimensionality reduction methods • Proficiency in ML model deployment through RESTful APIs, containerization, and container orchestration • Experience with SQL and modern data-processing or data-platform technologies. • Familiarity with statistical methods, experimental design, regression, classification, clustering, anomaly detection, and model evaluation • Programming skills in Python and experience with common data-science and machine-learning libraries is a plus • Familiarity with high-performance ML inference (CUDA, Libtorch, ONNX Runtime, C++ programming and data structures) is a strong plus • Experience with cloud-based ML training and inference using AWS, GCP, Azure, or Databrix is a plus • Familiarity with machine vision such as defect detection and OCR is a plus • Familiarity with big data frameworks such as Hadoop and Spark is a plus • Exposure to manufacturing, wafer fabrication, photonics , telecommunications is a plus • Demonstrated ability to take an analysis from problem definition through deployment, validation, and communication of results. Education & Experience • The Candidate will have minimum 5 years of experience with AI, preferably including ML, preferably with experience in statistical analytic techniques, and 3 years experience in a production environment. • A Bachelor’s degree in Electrical Engineering , Computer Science, Physics, or related field with specialization in Data Science, AI/ML, or Statistics ; a Master’s degree is preferred. Working Conditions The job is on-site for the first three months, with the possibility to convert to hybrid afterwards. The job requires weekly morning hours and occasional evening and weekend hours. Travel to other Coherent sites in the Bay Area and the US may be possible to share knowledge with other experts. Physical Requirements • Sitting or standing for several hours per day, with the option of having a sit-stand desk. • Extensive keyboard and mouse work. Safety Requirements All employees are required to follow the site EHS procedures and Coherent Corp. Corporate EHS standards. Quality and Environmental Responsibilities Depending on location, this position may be responsible for the execution and maintenance of the ISO 9000, 9001, 14001 and/or other applicable standards that may apply to the relevant roles and responsibilities within the Quality Management System and Environmental Management System. Culture Commitment Ensure adherence to company’s values (ICARE) in all aspects of your position at Coherent Corp.: I ntegrity – Create an Environment of Trust C ollaboration – Innovate Through the Sharing of Ideas A ccountability – Own the Process and the Outcome R espect – Recognize the Value in Everyone E nthusiasm – Find a Sense of Purpose in Work Coherent Corp. is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. If you need assistance or an accommodation due to a disability, you may contact us at talentacquisition@coherent.com . Job Summary: We are seeking an experienced Machine Learning Engineer to embed and scale AI/ML solutions within our manufacturing ecosystem to drive product development, yield improvement, screening, and process optimization. Our site is rapidly ramping and introducing new products, and this opportunity provides broad exposure to Development, Engineering, Manufacturing and Process Teams. The ideal candidate combines strong experience in AI/ML and data analysis with an interest in semiconductor wafer fabrication.

Requirements

Must-haves

Primary Duties & Responsibilities
Develop and validate models for yield improvement, screening accuracy, and process optimization through application of model selection and hyperparameter tuning.
Collaborate with Photonics designers and process, reliability and manufacturing engineers to align ML approaches with product objectives and to quantify cost-benefit analysis.
Deploy AI/ML within manufacturing systems through a combination of Edge AI, API serving, Containerization, and Cloud-based training and inference
Partner with industrial and MES software engineers to integrate AI/ML pipelines within existing production workflows
Develop reusable data pipelines, analytical tools, dashboards, and model-monitoring methods.
Support design of experiments, process characterization, and continuous improvement activities.
Help establish best practices for ML, and share the practices across the site and other sites.
The Candidate will have competency in the following areas:
Expertise in deep learning frameworks such as Pytorch, TensorFlow
Expertise deep learning architectures such as CNNs, RNNs, GANs
Expertise in ML methods such as Random Forests and Gradient Boosting
Experience with clustering, feature engineering, and dimensionality reduction methods
Proficiency in ML model deployment through RESTful APIs, containerization, and container orchestration
Experience with SQL and modern data-processing or data-platform technologies.
Familiarity with statistical methods, experimental design, regression, classification, clustering, anomaly detection, and model evaluation
Familiarity with high-performance ML inference (CUDA, Libtorch, ONNX Runtime, C++ programming and data structures) is a strong plus
Demonstrated ability to take an analysis from problem definition through deployment, validation, and communication of results.

Nice-to-haves

Programming skills in Python and experience with common data-science and machine-learning libraries is a plus
Experience with cloud-based ML training and inference using AWS, GCP, Azure, or Databrix is a plus
Familiarity with machine vision such as defect detection and OCR is a plus
Familiarity with big data frameworks such as Hadoop and Spark is a plus
Exposure to manufacturing, wafer fabrication, photonics , telecommunications is a plus
A Bachelor’s degree in Electrical Engineering , Computer Science, Physics, or related field with specialization in Data Science, AI/ML, or Statistics ; a Master’s degree is preferred.

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Keywords to mirror

mlaiapisqlpythonawsgcpazuremachine learning

Platform: oracle-cloud
Effort: medium

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