At CoMind, we are developing a non-invasive neuromonitoring technology that will result in a new era of clinical brain monitoring. In joining us, you will be helping to create cutting-edge technologies that will improve how we diagnose and treat brain disorders, ultimately improving and saving the lives of patients across the world.

SKILLS & EXPERIENCE:

• Git or Bitbucket for version control, including experience with managing versioned infrastructure-as-code (IaC) repositories
• CI/CD pipelines for automating workflows, including experience with integration testing and containerization pipelines
• Experience managing and orchestrating complex cloud workflows (e.g., ECS Tasks, AWS Batch), with a focus on event-driven and parallel processing
• Infrastructure as Code (IaC) experience (e.g., Terraform, AWS CloudFormation) for creating, maintaining, and scaling cloud infrastructure
• Docker for containerization, including experience with containerizing machine learning workflows and publishing containers to repositories like AWS ECR.

THE ROLE

CoMind is seeking a skilled Engineer to join our dynamic Research Data Science team to lead the orchestration of a robust ML training pipeline in AWS. This role is critical to enabling the scalable training and testing of a range of ML models on large volumes of a totally new form of clinical neuromonitoring data.

RESPONSIBILITIES:

• Architect and implement a scalable solution to support the Research Data Science Team in running a large number of assorted machine learning pipelines, including model training, evaluation, and inference
• Create a CI/CD pipeline for building containers from in-house Python packages, running integration tests, and publishing to AWS ECR
• Set up ECS or AWS Batch Tasks to run containers stored in AWS ECR
• Establish a robust configuration management system to store, version, and retrieve configurations associated with multiple machine learning workflows
• Implement robust error handling and monitoring solutions to ensure timely debugging across the pipeline with centralised logging and error reporting
• Implement cost monitoring solutions to track and manage compute costs across different runs, building dashboards to provide insights into resource usage and cost optimization
• Ensure security and data protection are integrated into the pipelines by applying AWS best practices for security protocols and data management
• Monitor and manage the team’s compute resources, including both cloud (AWS) and on-premise GPU nodes, ensuring efficient use and scalability
• Implement Infrastructure as Code (IaC) to set up and manage the pipeline architecture, using Terraform, AWS CloudFormation, or similar tools.