Machine Learning Engineer Jobs

About The Position

Requirements

• 2-5 years of professional experience with Python
• Experience with ML frameworks such as TensorFlow or PyTorch
• Experience preparing datasets, training models and evaluating performance
• Hands-on experience with NLP techniques and large language models (e.g., transformers)
• Proficiency with data science tools and programming languages (e.g., Python, R, SQL)
• Ability to communicate technical findings and issues clearly across teams
• Familiarity with deploying ML models to cloud-based environments (AWS preferred)
• Understanding of CI/CD concepts and how models move from experimentation to production
• Ability to identify and articulate deployment related issues without owning implementation
• Be able to work under supervision and accept feedback
• Familiarity with Microsoft Office Suite
• Relate effectively and work respectfully with diverse work groups
• Ability to consistently perform well during times of increased workload
• Set and meet deadlines
• Manage multiple job functions simultaneously

Nice To Haves

• Associates or Bachelor’s degree in Computer Science, Information Systems, Data Science or related technical field
• Hands-on experience working with large language models and prompt engineering (e.g., OpenAI)
• Understanding of the unique challenges and requirements of educational assessment data
• Experience working with version control (Git) and a collaborative development workflow

Responsibilities

• Design, train, evaluate and iterate on machine learning models to support DRC’s education analytics products
• Develop high-quality, maintainable Python code for model training, experimentation and evaluation workflows
• Collaborate closely with MLOps and DevOps engineers to ensure models are reliably deployed and operating in production environments
• Diagnose and communicate issues related to model performance, data quality, or deployment behavior
• Work with data scientists, psychometricians, and software engineers to support ML workflows and ensure scalable infrastructure for research and analytics.
• Contribute to best practices for model versioning, reproducibility and monitoring.