II-VI Aerospace & Defense · 4 months ago
Machine Learning Operations Contractor
41762 Christy Street, Fremont, CA, 94538, US
Full-time, Contract
Onsite
Senior Level
$50.96/hr - $101.92/hr
5+ years expII-VI Aerospace & Defense is seeking a Machine Learning Operations (MLOps) Contractor to drive semiconductor laser design and manufacturing excellence through AI/ML development. The role focuses on yield improvement, screening accuracy, and design optimization, requiring collaboration with researchers and engineers to enhance production processes.
AerospaceAppsManufacturingSpace Travel
Responsibilities
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
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
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
Qualification
Required
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
Preferred
Experience with NN architectures such as CNNs, AEs, GANs and with ensemble learning methods including gradient boosting and random forests
Familiarity with inverse design concepts
Background in photonics, semiconductor devices, or manufacturing yield optimization
Company
II-VI Aerospace & Defense
II-VI Aerospace & Defense provides optical assemblies.
201-500 employees
Funding
Current Stage
Growth StageLeadership Team
Sheila Campbell
Sr. Human Resources Business Partner
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