Machine Learning Engineer Intern

Key Skills

Algorithms, AWS, AWS Sagemaker, Continuous Integration & Continuous Delivery - CI/CD, DevOps, Docker, Machine learning techniques, MLflow, Python Programming, Terraform

Job Description

Position Responsibilities

• Work side-by-side with Data Engineers and Data Scientists to bring data and machine learning projects from prototype to production.
• Design and develop testing and integration tools with coding standards and software development best practices built-in. Test code, setup CI/CD pipeline and automation tools for DE and DS projects.
• Optimize machine learning model training processes.
• Develop and standardize logging and bench marking tools for machine learning workflow to improve traceability and visibility, drive insights and improve performance.
• Prepare release notes and configuration instructions, work with backend engineers to productionize the model release candidates.
• Participate in code reviews and design reviews.

Requirements

Location

• Remote or Fort Collins, Northern Colorado Area preferred

Experience

• Proficient in Python.
• Expertise with ML pipelines and algorithms. Familiarity with MLFlow, DVC is a plus.
• Experience implementing and deploying ML models.
• Knowledge of professional software development best practices including coding standards, code reviews, source control management, build processes, testing, and operations.
• Experience with AWS. Familiarity with Sagemaker is a plus.
• Experience with DevOps tools such as Terraform.
• Experience with virtual environments and docker.

ISO/QMS Requirements

• Understand and practice all requirements of EN ISO 13485:2016, ISO 13485:2016 MDSAP including 21 CFR 820, QMS Manual, Process Flows and Work Instructions. Experience with EN ISO 62304:2006 a plus.
• Comply with applicable regulatory requirements (including but not limited to MDSAP participating countries and CE Marking).
• Support Internal and External audits.

Benefits

Our people love working here because they are challenged by unique and difficult problems in a nimble startup environment that is backed by five plus years of success in performance.