Engineering Technician, Intermediate

Overview

Location:

San Diego, CA
Pay Rate: $22-28.56/hr on w2
Duration: 6 months (possible extension)

Job Description:

Education

4-year College Degree in engineering

Years of

Experience:

No expected experience after 4-year college degree.

Others

Python software coding skill is preferred.

• Capture high-quality photo and video content using camera devices (e.g., smartphone cameras), covering both camera lab test scenes and real‑world usage scenarios.
• Run image and video analysis tools on captured content to generate quantitative and qualitative analysis data; tools are primarily implemented in Python.
• Manage and maintain imaging databases across multiple Image Quality (IQ) domains, including texture/noise, color, HDR, exposure, zoom, bokeh, video quality, and stabilization.
• Configure and manage Android devices for content capture using Android Debug Bridge (ADB).
• (Optional) Develop and enhance database management tools using MATLAB and/or Python, including filename conventions and folder structures to support multi‑device workflows.
• Maintain and operate camera Image Quality (IQ) evaluation labs on the San Diego campus. Support the camera engineering team by ensuring lab readiness and efficient data collection workflows.
• [optional] IQ Evaluation Protocol Development Support:
  Develop the tools (coding in Python) for the extensive IQ analysis and support the IQ analysis activities for IQ evaluation protocol development on various camera IQ areas including texture/noise, color, HDR, exposure, zoom, bokeh, segmentation, video, stabilization.
• With good understanding of the Camera IQ characteristics, participate in activities to define and develop the photo/video evaluation scenarios for various Camera IQ areas, including lab test setup and real-life test scenes.
• The activities for IQ Evaluation Protocol Development Support also include handling the image/video tools such as ffmpeg and building up the database with training data set (image/video and their annotation) for ML networks.