1st. Creative Learning Academy Inc. is hiring: Machine Learning Engineer Intern

Content Summary: Machine Learning Engineer Intern at Reston, for 1st. Creative Learning Academy Inc.

Company Overview: SAIC is a premier technology integrator solving our nation's most complex modernization and readiness challenges across the defense, space, federal civilian, and intelligence markets. Our robust portfolio of offerings includes high-end solutions in systems engineering and integration, enterprise IT, cyber security, AI/ML, and data analytics.

This is a part-time remote job that can be worked anywhere within the U.S.

Key Responsibilities:

1. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions that leverage machine learning and data science techniques.
2. Assist in the development and deployment of machine learning models, algorithms, and data pipelines to solve complex problems and optimize processes.
3. Conduct data preprocessing, feature engineering, and exploratory data analysis to enhance model performance and insights.
4. Participate in designing and implementing experiments to evaluate model performance and iterate on improvements.
5. Assist in the integration of machine learning solutions into production systems, ensuring scalability, robustness, and reliability.
6. Stay up-to-date with the latest advancements in machine learning, artificial intelligence, and data science to propose innovative ideas and techniques for improving existing systems.
7. Contribute to documentation, knowledge sharing, and training to ensure the transfer of technical knowledge within the team.

Qualifications:

1. Must be a US Citizen.
2. Currently enrolled in an accredited university focusing in Computer Science, Engineering, Mathematics, or a related field.
3. Strong foundation in machine learning concepts and techniques, including supervised and unsupervised learning, feature selection, and model evaluation.
4. Proficiency in programming languages such as Python, and experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
5. Familiarity with data preprocessing, cleaning, and transformation techniques to handle real-world data challenges.
6. Basic understanding of statistics and experimental design for evaluating model performance and making data-driven decisions.
7. Exposure to version control systems (e.g., Git) and collaborative coding practices.
8. Strong problem-solving skills and the ability to work effectively in a team-oriented, fast-paced environment.
9. Excellent communication skills to convey complex technical concepts to non-technical stakeholders.
10. Previous coursework, projects, or internships in machine learning or data science is a plus.
11. Any experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies is advantageous.

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