Director of AI Engineering for Manufacturing/Quality

Altera•San Jose, CA

68d

About The Position

About Altera Altera is a global leader in FPGA and programmable logic solutions, enabling a broad range of markets including data center, communications, automotive, aerospace, and industrial. As we scale our global manufacturing and quality operations to support next-generation programmable devices, we’re seeking an AI-savvy engineering leader to transform the way we use data, algorithms and automation in fabrication, assembly, test, and quality systems. Role Summary In the role of Director of AI Engineering for Manufacturing & Quality, you will lead a global team responsible for architecture, development, deployment and scaling of artificial intelligence, machine learning and analytics solutions that enhance manufacturing yield, process reliability, quality assurance and operational productivity. You’ll collaborate with manufacturing, quality, supply chain, data/IT, and product engineering teams to build and integrate data-driven solutions across wafer fab, packaging/test, OSAT supply chain and final product delivery.

Requirements

Bachelor’s degree in Computer Science, Data Science, Electrical Engineering, Manufacturing Engineering, or a related field. Advanced degree (MS or PhD) preferred.
12+ years of experience in advanced analytics, machine learning, AI engineering or software engineering within manufacturing, quality, or semiconductor industry environments, with at least 5 years in a leadership or director-level role.
Demonstrated success developing and deploying AI/ML solutions at scale: predictive maintenance, visual inspection, yield optimization, defect reduction, or manufacturing productivity improvements.
Strong technical hands-on background: data science, ML model development, MLOps, computer vision, sensor data, edge computing, big data platforms.
Deep understanding of manufacturing operations, quality systems, semiconductor backend/OSAT flows, yield/yield-risk metrics, process control, automation, statistical process control.
Excellent strategic thinking, cross-functional leadership and business acumen: ability to translate manufacturing/quality problems into AI solutions, measure business value and drive change.
Excellent communication and stakeholder management skills: able to present to senior leadership, influence technical and business partners, and lead a global team.

Nice To Haves

Experience in semiconductor manufacturing or packaging/test environments (fab, OSAT, test floor, quality escapes).
Familiarity with FPGA or programmable logic device manufacturing flows, high-volume manufacturing, multi-site operations, supply chain constraints and quality challenges.
Experience with Industry 4.0 initiatives, smart factory, IoT sensor networks, digital twins, edge AI deployments.
Knowledge of cloud and on-premise data architectures, big data tools (Hadoop/Spark), ML platforms (TensorFlow, PyTorch), MLOps frameworks, and data governance.
Experience managing vendor/consulting engagements, AI tool evaluation & procurement, and building enterprise-grade AI platforms.
Prior experience scaling global engineering teams, driving culture change, and embedding analytics capability into manufacturing/quality functions.

Responsibilities

Define the vision and roadmap for AI/ML initiatives across manufacturing and quality domains (fab, packaging, test, OSAT, final product).
Build and lead a global team of data scientists, ML engineers, software engineers and manufacturing/quality-domain experts to deliver end-to-end AI solutions.
Architect infrastructure and platforms for large-scale data ingestion, real-time analytics, predictive models, root-cause diagnostics, anomaly detection, visual inspection, and process optimization.
Collaborate with manufacturing operations, packaging/test engineering, OSAT partners, quality assurance, reliability engineering, and supply chain teams to identify high-value use-cases (yield improvement, defect reduction, cycle time reduction, scrap minimization, predictive maintenance).
Oversee the design and deployment of machine learning models, computer vision systems (for visual inspection), advanced statistical analytics, digital twins, and process simulation, with the goal of converting data insights into action.
Drive integration of AI solutions into manufacturing lines and quality systems: pilot to scale to sustain, ensuring interoperability, monitoring, MLOps practices, model governance, data integrity, and business value realization.
Establish KPIs and dashboards to monitor performance of AI/ML solutions: yield lift, defect rate reduction, on-time release, cost per unit reduction, quality escapes, cycle-time improvements.
Provide regular executive updates on progress, ROI, and strategic alignment.
Build cross-functional partnerships with IT/data platform teams, manufacturing automation/industry 4.0 initiatives, supply chain analytics, and product engineering to ensure data architecture, tooling, and governance are aligned to AI strategy.
Identify, evaluate and deploy emerging technologies (edge AI, computer vision, deep learning, reinforcement learning, digital twin, IoT sensor fusion) to maintain competitive advantage and manufacturing leadership.
Manage budget, resource allocation, vendor/consulting relationships, and staffing for the AI engineering organization.
Cultivate a culture of innovation, experimentation, and continuous improvement: lead capability building, mentorship, and alignment of team goals with organizational objectives.