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

Improve detection accuracy across all object classes (person, vehicle, animal, package, face, license plate)

Reduce false positive and false negative rates

Fine-tune and retrain YOLO models on domain-specific datasets

Evaluate and integrate newer model architectures as they become available

Develop robust evaluation metrics and benchmarking pipelines

Model Efficiency & Optimization

Optimize inference speed and resource utilization

Implement model quantization, pruning, or distillation techniques

Balance accuracy vs. latency tradeoffs for real-time processing

Optimize frame skip strategies and batch processing

Profile and eliminate performance bottlenecks in the inference pipeline

Ongoing Development

Maintain and extend the existing detection pipeline

Add support for new object classes as business needs evolve

Improve hotzone (region-of-interest) detection logic

Enhance face recognition matching accuracy

Collaborate with the cloud team on infrastructure and scaling

Required Qualifications

Strong experience with object detection models , particularly YOLO family (v5 / v8 / v11), and understanding of model training, fine-tuning, and evaluation

Hands-on experience with PyTorch  for model development and optimization

Computer vision fundamentals : image / video processing, frame extraction, bounding box handling, NMS, confidence thresholds

Python proficiency  with production-quality code practices

Experience deploying ML models  in production environments (containerization, GPU inference, batching strategies)

Understanding of  model optimization techniques : quantization, TensorRT, ONNX conversion, etc.

Familiarity with  Linux / Docker / Kubernetes  environments

Preferred Qualifications

Experience with video analytics or surveillance systems

Background in face recognition systems

Experience with license plate recognition (ALPR / OCR)

Familiarity with zero-shot detection models (YOLOWorld, CLIP-based approaches)

Experience with dataset curation, annotation, and augmentation

Knowledge of edge deployment considerations (model size, latency constraints)

Experience with distributed job queues (Redis Queue, Celery)

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

Measurable improvements in detection accuracy (precision / recall) across object classes

Reduced inference time per video while maintaining or improving accuracy

Well-documented model evaluation pipelines and benchmarks

Proactive identification and resolution of model performance issues

Clear communication with the broader team on ML capabilities and limitations

Employment Details

Type : Full-time

Team : Cloud Infrastructure Team

Scope : Primary owner of CloudML video analytics platform