Camera Test Engineer, Machine Learning and Computer Vision
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Qualifications
Minimum qualifications:
- Bachelor's degree in Engineering, related field, or equivalent practical experience.
- Experience coding in Python/MATLAB/ C++.
- Experience with image quality evaluation and computer vision algorithm development.
Preferred qualifications:
- Experience with training deep learning neural networks and building the system from prototyping to production.
- Experience with machine learning framework (e.g. TensorFlow) and setting up model deployment environment using Docker.
- Experience with testing and building consumer electronics products from NPI to mass production.
- Knowledge of image processing algorithm and computer vision, including image registration, segmentation, classification, object detection, supervised/unsupervised learning.
- Familiarity with data structure and algorithms.
- Ability to travel up to 20% of the time as needed.
About the job
At Google, our philosophy is build it, break it and then rebuild it better. That thinking is at the core of how we approach testing at Google. Unlike roles with similar names at the other companies, Test Engineers at Google aren't manual testers -- you write scripts to automate testing and create tools so developers can test their own code. As a Test Engineer, you navigate Google's massive codebase, identify weak spots and constantly design better and creative ways to break software and identify potential problems. You'll have a huge impact on the quality of Google's growing suite of products and services.
Responsibilities
- Develop factory test stations with a focus on camera and/or automatic optical inspection solutions (AOI), including both hardware and software with a focus on assembly defects detection, and deploy the ML models and integrate into test station scripts.
- Develop entire computer vision solutions and collaborate with the Cloud Machine Learning (ML) team to invent machine/deep learning algorithms for production line from NPI to MP stage.
- Work with cross-functional teams, including assembly engineer and PD team to define test coverage and develop corresponding ML based defect detection algorithms. Work with OEM, ODM, JDM, and CM partners to ensure test stations are deployed across various factories.
- Analyze the build data (yield, overkill, escape rate) to improve the detection algorithm accuracy and stabilize station hardware setup involving camera optics and lighting condition.
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