Career Snapshot: Machine Learning Engineer
- Average Salary (U.S., 2025): ~$128,769 per year (ZipRecruiter)
- Typical Salary Range (All Levels): $101,500–$155,000 (25th–75th percentile)
- Senior / Specialized Roles: $157,000–$200,000+ in AI-focused industries (finance, healthcare, tech)
- Job Outlook (2024–2034): Employment of computer and information research scientists (which includes AI/ML specialists) is projected to grow +26%, much faster than average.
Essential Skills for Success
Students preparing for this pathway should focus on:
- Algorithms & Model Building: Regression, classification, clustering, neural networks
- Programming & Tools: Python, R, TensorFlow, PyTorch, scikit-learn
- Data Engineering Basics: Data pipelines, ETL, structured/unstructured data handling
- Model Evaluation & Tuning: Cross-validation, metrics, bias/variance tradeoff
- Deployment & MLOps Foundations: Monitoring, scaling, and maintaining models in production
- Visualization & Communication: Presenting insights and results to non-technical audiences
- Ethics & Interpretability: Building models that are fair, transparent, and responsible
Education Pathways
Many ML engineers pursue bachelor’s or master’s degrees in data science, statistics, computer science, or engineering. This beginner-level pathway, however, allows students to start developing ML skills much earlier.
For CTE programs, this can include:
- High school or dual credit courses in math, statistics, and computer science
- Hands-on projects in predictive modeling or simple AI applications
- Partnerships with universities or local businesses for mentorship and applied projects
- Perkins V alignment through stackable credentials, project-based assessments, and equity-focused access
Credentials & Certifications
The SchoolDay Academy Machine Learning Engineer Pathway (Beginner Level) includes curated certificates that give students hands-on experience in building and testing machine learning models:
- Google Machine Learning and AI Foundations Certificates
- Microsoft Azure AI Fundamentals (optional complement)
Additional credentials that may complement the pathway include:
- AWS Certified Machine Learning – Specialty
- TensorFlow Developer Certificate
- Microsoft Certified: Azure AI Engineer Associate
These certificates provide students with recognized credentials and a portfolio of real ML projects.
Related Career Pathways
Machine Learning Engineers often grow into specialized or leadership roles such as:
- Data Scientist
- AI Research Engineer
- Deep Learning Engineer
- MLOps Engineer
- Predictive Analytics Specialist
How to Get Started
For Students:
- Begin with the SchoolDay Academy Machine Learning Engineer Pathway, which combines beginner-level coursework, certifications, and project experience.
- Work on public datasets to build small prediction models and document them in a portfolio.
- Participate in competitions like Kaggle to practice applied ML problem-solving.
- Experiment with free tools like Google Colab and Jupyter notebooks to test algorithms.
- Shadow professionals in AI or data science roles to understand how models move from theory to production.
For CTE Directors and Districts:
- Embed the SchoolDay Academy pathway into tech or computing programs to ensure measurable credentials.
- Provide opportunities for students to run school or community data projects, such as forecasting attendance or analyzing performance data.
- Partner with local employers or universities for internships, mentorships, and guest projects.
- Ensure Perkins V alignment through dual credit, stackable credentials, and equity-focused access.
Why SchoolDay Academy Recommends This Pathway
SchoolDay Academy recommends the Machine Learning Engineer pathway because ML is central to the future of work across every sector. It blends technical depth with practical problem-solving, creating opportunities for innovation.
For students, it’s a high-paying, future-focused career path with strong demand.
For schools, it ensures measurable outcomes—certifications, portfolios, and project work—while aligning with Perkins V goals of stackable credentials, equity, and career readiness.
Conclusion
Machine learning engineering is one of the most exciting career pathways today—blending algorithms, data, and innovation to solve real-world problems. By guiding students into this pathway, districts can align with workforce demand while preparing learners for future-ready careers.
Explore the Machine Learning Engineer Career Pathway on SchoolDay Academy and start building your future today.