Organization: Zealogics
Location: Taiwan
We are multinational team to collaborate with Fortune 500 client ! As a C++ Engineer, you will develop and implement computer vision algorithms help SEM inspection and metrology tasks for various patterns/layers, improve system throughput and computation efficiency. Experiences: • 5+ year software development. Master degree and above. • Experience of image processing or pattern recognition algorithm development. Role and responsibilities: – Participate in algorithm development for inspection and metrology – Collaborate with architects, designers, and engineers to develop robust computer vision modules – Work with Product Management to prioritize feature development and breakdown tasks – Drive infrastructure and architectural optimization solutions – Perform code reviews and ensure proper design and delivery – Assessment and improve algorithm performance – Software development based on product/project requirements. – Inspection flow and inspection module performance evaluation and optimization. – Software troubleshooting and bug fixing. – Cross-nation and cross-team communication to understand the tasks and ensure projects are on time and teamwork smoothly. Education and experience: – MS. in exact sciences (Computer Sciences, Computer Engineering, Electrical/Electro-Optical Engineering, Applied Mathematics or Physics) – 3+ years of hand-on development as a computer vision engineer, Machine learning engineer or related role – Solid skills in C++ programming – Good communication and organizational skills – Experience transferring technology from research in computer vision, image processing or machine learning into a shipping product – Ability to work on complex projects and translate requirements from internal and external customer’s into algorithm specs – Familiar with Git/Bitbucket/JIRA. Nice to have: – image noise reduction – (Defect) detection and classification – Unsupervised/ Supervised learnings – Context-aware detection – Adaptive Threshold – Image filtering for CR/NR improvement – Image alignment Originally posted on Himalayas
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