Outdoor Robotics Computer Vision Intern

Laelaps AI; Zürich; Vor Ort; CHF 20.000 - 31.000; Teilzeit

Benefits dieser Stelle

Ownership stake

Mission-driven focus

Career growth potential

Collaborative team

Competitive compensation

International founding team

Zusammenfassung

Laelaps AI in Zurich is seeking a Computer Vision Intern to work on real‑time detection, tracking, and scene understanding for outdoor security robots. You will ship code to field hardware and learn how to translate research CV into production systems.

You will train models, build data pipelines, and evaluate field data, tackling edge cases like low light and weather. This hands‑on role emphasizes practical engineering and deployment in a startup setting.

Qualifikationen

• Master's degree in Computer Vision, ML, Robotics or related field.
• 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.
• Strong problem-solving mindset and eagerness to ship real systems.

Aufgaben

• Train, evaluate, and deploy object detection and tracking models for outdoor security scenarios.
• Build data pipelines that turn field 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

CV models

Python

Docker

Git

Problem solving

Ausbildung

Master's degree in Computer Vision / ML / Robotics

Tools

ROS 2

Jobbeschreibung

Laelaps AI in Zurich is seeking a Computer Vision Intern to work on real‑time detection, tracking, and scene understanding for outdoor security robots. You will ship code to field hardware and learn how to translate research CV into production systems.

You will train models, build data pipelines, and evaluate field data, tackling edge cases like low light and weather. This hands‑on role emphasizes practical engineering and deployment in a startup setting.