Edge AI Computer Vision Engineer for Real-Time Surveillance

Laelaps AI; Zürich; Vor Ort; CHF 120.000 - 190.000; Vollzeit

Benefits dieser Stelle

Competitive equity

Salary package

Zusammenfassung

Laelaps AI is building the perception stack for multi-sensor surveillance and robotics. You will own perception systems that detect, track, and re-identify objects across cameras, with real-time performance on edge hardware.

The role emphasizes turning off-the-shelf models into reliable live-site deployments, while training new models when needed. You should bring hands-on experience shipping computer vision or ML systems in production, with a focus on cross-camera ReID, object detection, and

Qualifikationen

• Proven track record shipping CV/ML systems to production.
• Experience with cross-camera ReID, object detection, and multi-object tracking.
• Hands-on with VLMs and real-time video streaming on edge hardware.

Aufgaben

• Design and ship cross-camera ReID for people and vehicles across distributed camera networks.
• Build high-throughput object detection and multi-object tracking pipelines for multiple video streams.
• Integrate vision-language models for open-vocabulary detection and scene understanding.
• Own the video ingestion/streaming path (RTSP, WebRTC) with low latency and resilience.
• Optimize and deploy models on edge hardware (TensorRT, quantization, pruning).
• Evaluate, fine-tune, and integrate open-source/commercial models; train custom models when needed.

Kenntnisse

Production CV/ML systems

Cross-camera ReID

Real-time video processing

Edge deployment

Python

PyTorch

VLMs

Tools

TensorRT

quantization

WebRTC

Jetson

PyTorch

DeepStream

Jobbeschreibung

Laelaps AI is building the perception stack for multi-sensor surveillance and robotics. You will own perception systems that detect, track, and re-identify objects across cameras, with real-time performance on edge hardware.

The role emphasizes turning off-the-shelf models into reliable live-site deployments, while training new models when needed. You should bring hands-on experience shipping computer vision or ML systems in production, with a focus on cross-camera ReID, object detection, and