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Video Machine Learning EngineerMarkiTech • Toronto, Ontario, Canada
Video Machine Learning Engineer

Video Machine Learning Engineer

MarkiTech • Toronto, Ontario, Canada
Il y a 2 jours
Salaire
55 000,00 $CA par an
Type de contrat
  • Temps plein
  • Temporaire
  • Quick Apply
Description de poste
Company: CliniScriptsContract: Initial six months, with the possibility of extensionCompensation: Approximately $60,000 annual equivalent, subject to final confirmationLocation: [Remote/Hybrid]Reports to: CTO
About the Role:CliniScripts is seeking a Video Machine Learning Engineer to support an ABA therapy video-data project.The engineer will help build a secure pipeline for processing recorded therapy sessions and developing computer-vision and machine-learning models using BCBA-annotated video data.
Responsibilities:
  • Review the existing video dataset and determine its suitability for ML training.
  • Build a secure pipeline for uploading, storing, processing, and tracking videos.
  • Create automated checks for video, audio, and metadata quality.
  • Connect timestamped BCBA annotations to video data.
  • Develop features for pose, movement, speech, and behavioural-event analysis.
  • Build and evaluate proof-of-concept video ML models.
  • Document the technical process, model results, and dataset limitations.
  • Follow strict privacy and security requirements when handling videos involving children.

Requirements:
  • Strong Python and machine-learning experience.
  • Practical experience with video, computer vision, and tools such as OpenCV or FFmpeg.
  • Experience with PyTorch, TensorFlow, or a similar ML framework.
  • Experience with pose estimation, action recognition, or video classification.
  • Understanding of ML evaluation, annotation quality, and dataset bias.
  • Ability to work independently and communicate with technical and clinical teams.

Initial DeliverablesDuring the six-month contract, the engineer will be expected to:
  • Audit the current video dataset.
  • Recommend video-quality and data-collection standards.
  • Build the initial video-processing and quality-control pipeline.
  • Create a baseline feature-extraction workflow.
  • Develop and evaluate an initial proof-of-concept model.



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