Machine Learning Engineer - Video Analytics

About Ezlo Innovation

Ezlo Innovation is a leading IoT platform developer powering smart home and property management solutions across 60+ countries. Our family of brands—including Vera, MiOS, Fortrezz, and Centralite—brings nearly 50 years of combined experience in home automation and IoT markets. We deliver cloud-to-ground, white-label IoT solutions to security dealers, property management companies, builders, utilities, and retail partners worldwide.

About the Role

We're looking for a Machine Learning Engineer to own and advance our CloudML video analytics platform. This system processes video from smart home cameras to detect objects including people, vehicles, animals, packages, faces, and license plates. You'll be the primary developer responsible for this project while collaborating with our broader cloud infrastructure team.

This role focuses heavily on  ML model performance —improving detection accuracy, reducing false positives / negatives, and optimizing model efficiency for real-time video processing at scale.

The Platform

CloudML v2 is a production video object detection service built on :

• ML / Computer Vision : YOLO v8 / v11, YOLOWorld (zero-shot detection), PyTorch, OpenCV, face-recognition, OpenALPR
• Backend : Python 3.11, FastAPI, Redis Queue (RQ)
• Infrastructure : Kubernetes, Helm, Docker, NVIDIA GPUs (CUDA 12.6)
• Observability : OpenTelemetry, structured logging

The system processes MP4 video files through a multi-stage pipeline : download, validation, frame extraction, multi-model inference (standard objects, packages, faces, license plates, barcodes), and webhook delivery of results.

Primary Responsibilities

Model Performance & Accuracy

Model Efficiency & Optimization

Ongoing Development

Required Qualifications

Preferred Qualifications

Tech Stack You'll Work With

Category

Technologies

ML Frameworks

PyTorch, Ultralytics YOLO, YOLOWorld, OpenCV, Supervision

Specialized ML

face-recognition (dlib), OpenALPR, Tesseract OCR, pyzbar

Backend

Python 3.11, FastAPI, Redis, RQ (Redis Queue)

Infrastructure

Kubernetes, Helm, Docker, GitLab CI / CD

Hardware

NVIDIA GPUs, CUDA 12.6

Observability

OpenTelemetry, structured logging

What Success Looks Like

Employment Details