Computer Vision Intern — Field-Ready Autonomy & Edge AI

Blackshark; Zürich; Vor Ort; CHF 17.000 - 28.000; Teilzeit

Benefits dieser Stelle

Ownership

Mission-aligned work

Career path

PhD-level team

Competitive equity/salary

International founding team

Zusammenfassung

Laelaps AI in Zurich is seeking a Computer Vision Intern to work hands-on with our perception stack, building real-time detection, tracking, and scene understanding for outdoor security robots.

You will train models, build data pipelines, and evaluate field data, shipping production-quality CV code on real hardware. This internship offers practical experience turning research into deployable systems.

Qualifikationen

• Currently pursuing or recently completed a Master's in CV/ML/Robotics.
• Strong PyTorch (or JAX) skills and experience training/evaluating CV models.
• Familiarity with modern object detection and tracking architectures.
• Comfortable using Docker and Git in workflows.
• Good coding skills in Python.

Aufgaben

• Train, evaluate, and deploy object detection and tracking models for outdoor security scenarios.
• Build data pipelines that turn field-captured video into labeled training datasets.
• Run experiments on edge cases: low light, motion blur, weather, occlusion.
• Optimize models for edge deployment: quantization, pruning, latency tuning.
• Contribute to ROS 2 integrations and the wider autonomy pipeline.
• Apply solid engineering practices: Docker, version control, reproducible experiments.

Kenntnisse

PyTorch

JAX

Python

Object Detection

Tracking

Problem Solving

Ausbildung

Master's degree in Computer Vision, ML, Robotics or related

Tools

Docker

Git

ROS 2

Jobbeschreibung

Laelaps AI in Zurich is seeking a Computer Vision Intern to work hands-on with our perception stack, building real-time detection, tracking, and scene understanding for outdoor security robots.

You will train models, build data pipelines, and evaluate field data, shipping production-quality CV code on real hardware. This internship offers practical experience turning research into deployable systems.