Vision Systems Engineer 4

In the Global Products Group, we are dedicated to excellence in the design and engineering of Lam's etch and deposition products. We drive innovation to ensure our cutting-edge solutions are helping to solve the biggest challenges in the semiconductor industry. At Lam, as a Product Engineer, you thrive in high-stakes, dynamic environment, driving product development at the forefront of the semiconductor equipment industry. Collaborating closely with customers and internal teams, you will provide real-time problem-solving to customer challenges and to enhance our products. Your passion for engaging with customers and innovative engineering drives progress in our industry, tackling the once unsolvable. In this role, you will directly contribute to Equipment Intelligence team in The Deposition Group.

Responsibilities

Design, develop, and deploy computer vision algorithms for detection, classification, segmentation, anomaly detection, and predictive analytics; Build scalable machine learning and deep learning models using image, video, and multimodal sensor data; Lead the complete data science lifecycle, including data collection, labeling, feature engineering, model training, validation, deployment, and monitoring; Develop robust image-processing pipelines utilizing modern computer vision frameworks and cloud technologies; Define data quality standards and establish model performance metrics to ensure production readiness; Collaborate with process engineers, software developers, hardware engineers, and product teams to deliver integrated vision solutions; Conduct root-cause analysis and use advanced analytics to improve yield, process stability, and equipment performance; Drive innovation by evaluating emerging AI, machine learning, and computer vision technologies; Mentor junior engineers and provide technical guidance on best practices in data science and machine learning.

Requirements

Master's degree in Data Science, Computer Science, Physics, or related field with 8+ years of relevant experience; or Ph.D. with 5+ years of experience; Computer Vision; Machine Learning and Deep Learning; Statistical Modeling and Experimental Design; Image Processing and Pattern Recognition; Proficiency C/C++, in Python and scientific computing libraries such as TensorFlow and/or PyTorch; OpenCV; Experience developing and deploying production AI/ML systems; Strong knowledge of CNNs, object detection, segmentation, and anomaly detection methodologies; Experience with cloud platforms (Azure, AWS, or GCP) and MLOps practices; Ability to handle large-scale structured and unstructured datasets.

Nice to have

Experience in semiconductor manufacturing, industrial automation, robotics, medical imaging, or advanced inspection systems; Knowledge of optical systems, cameras, sensors, and imaging hardware; Experience with edge AI deployment and real-time inference systems; Familiarity with SQL, Spark, Databricks, and distributed computing environments; Increased process efficiency, yield, or product quality through AI-driven insights; Successful deployment and adoption of vision solutions in production environments; Technical leadership and influence across multiple engineering programs.