Job Description: AI Engineer

The AI Engineer is responsible for end-to-end development and deployment of AI-powered data products based on machine learning over large data sets. The AI Engineer will be responsible for model design, evaluation, and production rollout using standardized Stride coding best practices.

The AI Engineer will provide technical guidance to cross-functional teams and develop an understanding of Stride’s business objectives, using AI to improve student retention and academic outcomes. The ideal candidate is deeply experienced, passionate about solving problems through applied ML techniques, comfortable manipulating and summarizing data, and able to set priorities and facilitate collaborative work on complex projects.

Essential Functions:

• Applies expertise in applied AI, model, API, and pipeline creation, as well as the presentation of data to see beyond the numbers and understand how our users interact with our core products.
• Provides strategic influence with Product and Engineering teams to solve problems and identify trends and opportunities.
• Collaborates with IT managers, data stewards, and data engineers to ensure that AI workflows are efficiently provisioned with high-quality data and follow best practices in security, privacy, and scalability.
• Informs, influences, and supports product direction by translating business needs into innovative yet practical AI solutions that drive measurable outcomes.
• Ensures that AI output is measurable, documented, reproducible, and actively monitored for performance drift.

Qualifications:

• Bachelor’s degree in Computer Science, Math, Physics, Engineering, or related quantitative field AND Six (6) years’ related experience; OR Equivalent combination of education and experience.
• Hands-on experience with modern ML libraries (e.g., Python-based frameworks such as TensorFlow, PyTorch, scikit-learn) and understanding of statistical principles.
• Proficiency with AWS, Azure, Docker, Kubernetes, and Terraform to build scalable, secure, and high-performing environments.
• Ability to design, implement, and maintain robust ML pipelines, including version control, containerization (Docker, Kubernetes), and CI/CD processes.

Work Environment:

This is a home-based position. The work environment characteristics described here are representative