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Video Machine Learning Engineer

Summary

Develops video ML models and pipelines for analyzing ABA therapy sessions, focusing on pose/movement/speech analysis while ensuring strict privacy and data quality standards.

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:

  • 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 Deliverables

During 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.

See also

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