Drimva

Simbe Robotics

Computer Vision / Applied AI Intern — Summer 2027

San Francisco Bay Area · On-site · Internship · Posted Oct 14, 2026

Education
Currently pursuing a BS, MS, or PhD in Computer Science, Computer Engineering, Robotics, Machine Learning, Data Science, or a related technical field.

About the job

Develop and evaluate computer vision and machine learning systems that turn imagery from retail robots into actionable retail intelligence.

What you'll do

  • Develop, test, and evaluate computer vision and machine learning solutions using Python and modern ML frameworks.
  • Analyze production image datasets, identify failure modes, and improve tools and workflows for model evaluation and experimentation.
  • Own a scoped project, collaborate with technical teams, and present findings and results.

What they ask for

  • Currently pursuing a BS, MS, or PhD in a related technical field; primarily targeting final-year students and also considering strong juniors.
  • Strong Python skills and experience with structured Python projects beyond notebooks.
  • Coursework or hands-on experience in computer vision, machine learning, or deep learning; experience with PyTorch, TensorFlow, or a comparable framework.
  • Familiarity with Git or another source-control system, and ability to evaluate results and investigate failures.

Nice to have

  • Projects, internship, research, or other experience involving computer vision, machine learning, robotics, or software.
  • Experience with vision tasks, image datasets, model evaluation, or tools such as pandas, NumPy, OpenCV, Jupyter, SQL, Linux, Docker, or AI-assisted coding tools.

Pay

USD 48–48 per-hour-wage

Tools

Python, PyTorch, TensorFlow, Git, pandas, NumPy, OpenCV, SQL

AI focus

Apply computer vision and machine learning to production imagery from autonomous retail robots, including tasks such as object detection, OCR, barcode decoding, and segmentation.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 5, 2026 (UTC)