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Markitech · Toronto, ON M5J 2P1

Video Machine Learning Engineer

Hybridfull time$60,000 / yearPosted Aug 12
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Stack mentioned

pythonpytorchtensorflowmachine-learningcomputer-vision

Company: CliniScripts

Contract: Initial six months, with the possibility of extension

Compensation: Approximately $60,000 annual equivalent, subject to final confirmation

Location: [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:

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Review the existing video dataset and determine its suitability for ML training.

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Build a secure pipeline for uploading, storing, processing, and tracking videos.

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Create automated checks for video, audio, and metadata quality.

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Connect timestamped BCBA annotations to video data.

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Develop features for pose, movement, speech, and behavioural-event analysis.

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Build and evaluate proof-of-concept video ML models.

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Document the technical process, model results, and dataset limitations.

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Follow strict privacy and security requirements when handling videos involving children.

Requirements:

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Strong Python and machine-learning experience.

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Practical experience with video, computer vision, and tools such as OpenCV or FFmpeg.

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Experience with PyTorch, TensorFlow, or a similar ML framework.

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Experience with pose estimation, action recognition, or video classification.

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Understanding of ML evaluation, annotation quality, and dataset bias.

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Ability to work independently and communicate with technical and clinical teams.

Initial Deliverables

During the six-month contract, the engineer will be expected to:

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Audit the current video dataset.

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Recommend video-quality and data-collection standards.

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Build the initial video-processing and quality-control pipeline.

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Create a baseline feature-extraction workflow.

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Develop and evaluate an initial proof-of-concept model.

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