AI Engineer for Manufacturing/Quality

Altera•San Jose, CA

110d

About The Position

About Altera Altera is a global leader in FPGA and programmable semiconductor technology. We power innovation across data center, communications, automotive, aerospace, and industrial solutions. Join us as we accelerate transformation across our world-class manufacturing and quality operations using advanced AI and data solutions. Role Summary As an AI Engineer for Manufacturing & Quality at Altera, you will design, develop and deploy scalable artificial intelligence and machine learning solutions that improve manufacturing yield, enhance product quality, optimize test and inspection, and increase operational efficiency across wafer fabrication, assembly/test, and our OSAT supply chain. You will work closely with manufacturing, quality assurance, automation, and data platform teams to convert data into actionable insights that directly improve business results.

Requirements

Bachelor’s degree in Computer Science, Data Science, Electrical Engineering, Manufacturing Engineering, or related field; Master’s preferred.
10+ years of industry experience in data science, machine learning, or advanced analytics, ideally in semiconductor manufacturing, test, or quality domains.
Proven hands-on experience developing and deploying machine learning solutions into production environments.
Proficiency in: ML frameworks (TensorFlow, PyTorch, Scikit-learn) Data engineering and big-data systems (Spark, Kafka, SQL/NoSQL) Statistical modeling, anomaly detection, SPC, predictive modeling, and root-cause analysis Python programming and industrial data processing
Experience working with manufacturing data types: fab or OSAT process data, test data, inline sensor data, equipment telemetry, quality metrics.
Strong capability translating engineering challenges into data-driven models with measurable results.
Excellent problem-solving, communication, and stakeholder-collaboration skills.

Nice To Haves

Semiconductor backend and test engineering experience (OSAT flow, yield loss mechanisms, parametric data analysis).
Computer vision experience in manufacturing environments (deep learning-based inspection, AOI systems).
Familiarity with MLOps, model governance, and deployment tools (Docker, Kubernetes, CI/CD).
Working knowledge of Industry 4.0 smart manufacturing concepts, digital twins, and IoT data platforms.
Experience with FPGA manufacturing, reliability controls, and high-volume product ramps.

Responsibilities

Develop machine learning and advanced analytics models for yield optimization, defect prediction, anomaly detection, and quality improvement across semiconductor manufacturing processes.
Build and scale computer vision systems for automated visual inspection, defect classification, and inline quality controls.
Work closely with process engineers, test engineers, and quality teams to identify, analyze, and prioritize high-impact use cases for AI applications.
Perform advanced statistical analysis and data mining to uncover trends, correlations, and root-cause signals in complex manufacturing datasets.
Deploy and integrate AI/ML solutions into production environments following MLOps best practices, ensuring model robustness, observability, versioning, and sustainment.
Partner with IT/data platform teams to implement clean data pipelines, sensor data integration, and real-time analytics capabilities.
Build dashboards and KPIs to monitor deployed AI solutions and quantify business impact (yield lift, cycle-time reduction, scrap reduction, cost savings).
Conduct continuous model improvement and retraining cycles to maintain model accuracy and adaptability to process and product changes.
Document models, workflows, data sources, and technical decisions for traceability and engineering rigor.
Remain current with emerging AI technologies, quality methodologies, and semiconductor manufacturing trends to propose innovation opportunities.