This is a fully remote position, open to applicants in Canada.

📋 Description

• Take charge of the core aspects of our machine learning lifecycle, ensuring that data pipelines are automated, reproducible, and optimized for high performance at scale.

• Facilitate seamless model training, deployment, and monitoring within intricate, multimodal systems, advancing the progression of innovative AI/ML applications.

• Collaborate with ML partners, Annotation engineers, and TPMs to define infrastructure and training specifications.

• Design and uphold robust CI/CD and Continuous Training (CT) pipelines for sophisticated multimodal models.

• Establish versioning and storage protocols for extensive 2D/3D datasets to guarantee reproducibility and efficient access.

• Implement and oversee systems to monitor model performance and track data drift in live production settings.

⛳️ Requirements

• A Master's degree in Computer Science, Engineering, Mathematics, or a related discipline.

• At least 5+ years of pertinent industry experience, preferably in a dynamic, high-growth technology sector.

• Demonstrated experience as an MLOps Engineer in a fast-moving applied machine learning environment.

• Previous experience in industries characterized by complex multidisciplinary teams such as robotics, smart grids, precision agriculture, game development, or aerospace.

• Proficiency in Python, Git, and the Unix shell.

• Extensive knowledge of Docker, Kubernetes, and workflow orchestration tools (e.g., Airflow, Prefect, or Kubeflow).

• Familiarity with collaborative platforms such as Jira/Confluence, Slack, and a Git server.

• Strong Mathematical foundation: Preferred for comprehending the resource requirements of 3D data transformations.