Job description

We are seeking a Machine Learning Operations (MLOps) Contractor to drive semiconductor laser design and manufacturing excellence through the development and deployment of AI/ML within production. The emphasis will be on yield improvement, screening accuracy, and design optimization. The MLOps Contractor will develop and validate models based on extensive production data and deploy scalable models through a combination of cloud-based infrastructure and on-premises high performance inference. This position represents a unique opportunity to make an impactful contribution to the laser manufacturing industry and to pioneer AI/ML integration across Coherent international manufacturing sites. This is a contract-based position, ideal for a data scientist or engineer with strong AI/ML expertise and a background in photonics, semiconductor device physics, or semiconductor manufacturing. Primary Duties & Responsibilities Data Processing and Model Development • Enhance data pipelines across wafer processes, die-level test, and experimentation. • Develop supervised and unsupervised models for yield prediction and performance screening. • The ideal candidate will be 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. Validation and Deployment • 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. Familiarity with inverse design concepts is a plus. Collaboration and Reporting • Work closely with photonics researchers and process engineers to align ML approaches with experimental and production objectives and quantify cost-benefit analysis. • Document methodologies, model performance, and research findings clearly. • Provide regular updates and deliverables to project stakeholders. Education & Experience • B.S. with 5-year industry experience or M.S./ Ph.D. with 2-year industry experience in Electrical Engineering, Computer Science, Physics, Photonics, or related field. • Proven track record in AI/ML deployment and integration within high visibility projects. Skills • 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 • Ability to visualize and communicate insights effectively • Background in photonics, semiconductor devices, or manufacturing yield optimization is a strong plus Working Conditions • On-site • 6-12 months temporary assignment 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:

Requirements

Must-haves

Primary Duties & Responsibilities
Data Processing and Model Development
Enhance data pipelines across wafer processes, die-level test, and experimentation.
Develop supervised and unsupervised models for yield prediction and performance screening.
The ideal candidate will be proficient in data pre-processing, feature extraction, dimensionality reduction, clustering, classification, and regression.
Validation and Deployment
Apply model selection to identify effective approaches and validate models using known techniques.
Collaboration and Reporting
Work closely with photonics researchers and process engineers to align ML approaches with experimental and production objectives and quantify cost-benefit analysis.
Document methodologies, model performance, and research findings clearly.
Provide regular updates and deliverables to project stakeholders.
Education & Experience
B.S. with 5-year industry experience or M.S./ Ph.D. with 2-year industry experience in Electrical Engineering, Computer Science, Physics, Photonics, or related field.
Proven track record in AI/ML deployment and integration within high visibility projects.
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
Ability to visualize and communicate insights effectively
Background in photonics, semiconductor devices, or manufacturing yield optimization is a strong plus
Working Conditions
6-12 months temporary assignment

Nice-to-haves

Experience with NN architectures such as CNNs, AEs, GANs and with ensemble learning methods including gradient boosting and random forests is preferred.
Integrate domain knowledge from semiconductor laser physics into ML models for improved interpretability. Familiarity with inverse design concepts is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlpythonawsgcpazurekubernetes

Platform: oracle-cloud
Effort: medium