(Sr.) C++ Developer / Image Processing - Fully Remote(WFH)
Zealogics.com · Remote
Checked against jazzhr. Still accepting applications.
- Location
- Remote
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
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