Machine Learning Engineer Intern

We are looking for an entry-level Machine Learning Engineer Intern to bridge the gap between data science research and production-ready machine learning solutions and pipelines. In this role, you will collaborate closely with our Assessment Innovation team to translate research models into efficient, scalable, and well-documented code. Additionally, you will work alongside our engineers to ensure machine learning solutions align with best engineering practices, performance standards, and infrastructure requirements. 

Key Responsibilities: 

1. ML Code Development & Optimization 

• Implement and optimize ML pipelines based on prototypes provided by data scientists. 
• Refactor, modularize, and enhance research code for production-grade deployment. 
• Ensure scalability, efficiency, and maintainability of ML pipelines. 

2. Collaboration with Data Scientists 

• Assist in transforming research algorithms into well-structured, reusable, and optimized code. 
• Provide software engineering expertise to improve data scientist-developed scripts and notebooks. 

3. Engineering & Infrastructure Support 

• Work closely with the principal engineer to integrate ML models into production systems. 
• Develop and maintain ML pipelines, APIs, and data processing workflows. 

4. Testing & Documentation 

• Write comprehensive unit and integration tests for ML models and pipelines. 
• Document model implementation details, assumptions, and performance benchmarks. 
• Maintain version control and code reviews for reproducibility and collaboration.

5. Performance Monitoring & Maintenance 

• Deploy and monitor machine learning models in production environments. 
• Debug and optimize model performance and data quality issues. 

• Bachelor’s degree in Computer Science, Data Science, Machine Learning, or a related field. 
• 1+ years of experience in machine learning engineering, software development, or a similar role. 
• Proficiency in Python and ML frameworks such as PyTorch. 
• Strong experience in software engineering best practices (e.g., clean code, modularity, version control). 
• Familiarity with Azure and ML deployment systems (Databricks). 
• Strong problem-solving skills, attention to detail, and ability to work in a cross-functional team. 
• Knowledge of MLOps best practices for CI/CD, model tracking, and automated retraining. 

Why Join Us? 

• Work in a collaborative environment with data scientists and engineering leadership. 
• Tackle real-world machine learning challenges and contribute to cutting-edge projects. 
• Opportunity to grow your ML engineering expertise and influence production ML systems. 

Salary: The anticipated hiring range for this position is listed below.

Variable Pay: No

The exact compensation offered will vary based on skills, experience, and geographic location.

Benefits: Click here for an overview of the benefits DDI offers.