Machine Learning Operations Engineer (MLOps)

Job Description

Job Description

Work Setting: 100% onsite engineering and manufacturing environment in Fremont, CA

Employment Type: Contract (40 hours per week)

Compensation: $50–$100 per hour

Benefits: Contractor position; conversion to full-time may be possible based on project success and business needs

Position Summary

A global leader in photonics and semiconductor technology is seeking a Machine Learning Operations (MLOps) Engineer to help develop, deploy, and scale AI/ML solutions within advanced manufacturing operations.

This is a highly visible, cross-functional role focused on applying machine learning and artificial intelligence to improve manufacturing yield, process control, defect detection, and operational efficiency. The successful candidate will work directly with Process Engineering, Product Engineering, Test Engineering, Manufacturing, MES, and IT teams to build data pipelines, develop machine learning models, and deploy production-ready AI solutions into manufacturing workflows.

This role offers a rare opportunity to pioneer AI/ML capabilities within a cutting-edge semiconductor and photonics manufacturing environment while directly impacting yield improvement and cost reduction initiatives.

What You'll Do

• Partner with Process, Product, and Test Engineering teams to understand manufacturing workflows, data sources, and business objectives

• Develop, train, validate, and optimize machine learning models for manufacturing applications

• Build and maintain reliable data pipelines supporting model development and deployment

• Apply supervised and unsupervised learning techniques to improve process control, yield, and defect detection

• Define, monitor, and report KPIs related to model performance and manufacturing outcomes

• Deploy machine learning models into production environments using APIs, containers, and orchestration platforms

• Integrate AI/ML solutions with existing manufacturing systems, databases, MES platforms, and on-premise infrastructure

• Collaborate with Operations and Engineering stakeholders to identify new AI/ML opportunities

• Monitor model performance, retrain models as necessary, and drive continuous improvement initiatives

• Document methodologies, validation approaches, performance results, and improvement plans

• Support knowledge transfer and collaboration with partner manufacturing sites deploying similar AI/ML solutions

What You Bring

• Bachelor's degree in Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, Data Science, Machine Learning, or related field required

• 5+ years of relevant experience, or Master's degree with 2+ years of experience

• Strong expertise with at least one deep learning framework such as PyTorch, TensorFlow, or Keras

• Experience with deep learning architectures such as CNNs, RNNs, VAEs, GANs, or related models

• Experience with tree-based learning methods including Random Forests, Gradient Boosting, or similar approaches

• Strong understanding of data preprocessing techniques including normalization, denoising, feature engineering, and missing data handling

• Experience with model development best practices including hyperparameter tuning, overfitting prevention, model validation, and k-fold cross-validation

• Experience deploying machine learning models using REST APIs, containerization, and orchestration technologies

• Strong Python programming and data analysis skills

• Ability to work effectively across engineering, manufacturing, and operations teams

• Proven track record of developing and deploying production-ready AI/ML solutions

Preferred Qualifications

• Experience with CUDA, ONNX, LibTorch, C++, and high-performance inference environments

• Experience with machine vision, computer vision, OCR, defect detection, or image analytics

• Knowledge of clustering, dimensionality reduction, and feature extraction techniques

• Familiarity with AWS, Azure, GCP, or cloud-based AI/ML environments

• Semiconductor manufacturing experience

• Experience supporting manufacturing, quality, yield improvement, or industrial AI applications

• Experience working with large manufacturing datasets and operational analytics

What You Get

• Opportunity to build one of the first dedicated AI/ML programs within a major semiconductor manufacturing operation

• Direct impact on yield improvement, manufacturing efficiency, and product quality

• Exposure to cutting-edge photonics and optical networking technologies supporting AI infrastructure growth

• Highly visible role with significant cross-functional collaboration

• Opportunity to influence manufacturing operations on a global scale

• Strong technical autonomy and ownership

• Potential pathway into a long-term AI/ML leadership role based on performance and business growth

• Collaborative environment with experienced engineering, manufacturing, and product development teams

• Opportunity to apply advanced machine learning techniques to real-world industrial challenges