MasterControl is looking for a Machine Learning Ops Intern to contribute to various projects focused on applying machine learning to solve real-world problems in life sciences and other regulated industries.
Requirements
- Strong Python skills, with interest in infrastructure, automation, or ML deployment
- Exposure to machine learning libraries (e.g., PyTorch, TensorFlow, Hugging Face)
- Familiar with Linux command line, Git, Docker, and cloud platforms (AWS preferred)
- Bonus: any experience with Kubernetes, CI/CD tools, or GPU workloads
- Strong desire to learn modern MLOps tools and practices
Responsibilities
- Assist in setting up CI/CD pipelines for ML workflows using GitHub Actions, Argo Workflows, or similar tools
- Work with the team to containerize and deploy ML models and notebooks to Kubernetes-based environments
- Help monitor and debug running ML workloads using tools like Prometheus, Grafana, or OpenTelemetry
- Contribute to infrastructure-as-code (IaC) setups with Terraform, Helm, and Kustomize
- Collaborate with ML engineers and data scientists to improve training and inference pipelines
- Support GPU-enabled model training and inference jobs in cloud (e.g., AWS EKS)
- Document deployment workflows, system architecture, and experiment tracking processes
Other
- Currently pursuing a degree in Computer Science, Data Engineering, AI/ML, or a related technical field
- Clear communicator, self-driven, and collaborative team player
- Applicants must be currently authorized to work in the United States on a full-time basis
- Must be enrolled at Northeastern University and eligible to participate in the official Co-op program for the designated term
