Aerospace ML Co-Op — Onsite Spring 2027 (Parsippany)

Marotta Controls, Inc.; Parsippany-Troy Hills (NJ); On-site; USD 25,000 - 33,000; Full time

Job summary

Marotta Controls, Inc. in Parsippany, NJ is seeking a Spring/Summer 2027 Machine Learning Co-Op to gain hands-on data-driven engineering experience.

You will work on a machine learning project within the Power and Actuation Systems unit, applying Python, data analysis, and ML concepts to real-world aerospace/defense challenges. You’ll receive mentorship, collaborate with engineers, and present findings to senior leadership at rotation end.

Qualifications

• Enrolled in a data science/CS/engineering degree program.
• Strong GPA and coursework in engineering/computing/math.
• ML/Data science coursework or projects required.
• Proficient in Python and ML libraries.
• Familiar with data analysis libraries like NumPy/Pandas, scikit-learn.
• Understand basic ML concepts: training, evaluation, etc.
• Experience with datasets and projects.
• Strong analytical, writing and communication skills.
• Available for full-time onsite Jan 2027 through Summer 2027.

Responsibilities

• Support development and implementation of a machine learning project.
• Clean and analyze engineering data for ML models.
• Develop, train, test, and evaluate ML algorithms and models.
• Write and maintain data processing and testing code.
• Evaluate model performance and identify improvement opportunities.
• Create visualizations and technical summaries of findings.
• Document methodologies and results.
• Collaborate with cross-functional teams to translate problems into data-driven solutions.
• Present progress and results to engineering leadership.

Skills

Data science

Python

Machine learning

NumPy

Pandas

scikit-learn

ML fundamentals

Education

Pursuing BS in Data Science

Tools

TensorFlow

PyTorch

Job description

Marotta Controls, Inc. in Parsippany, NJ is seeking a Spring/Summer 2027 Machine Learning Co-Op to gain hands-on data-driven engineering experience.

You will work on a machine learning project within the Power and Actuation Systems unit, applying Python, data analysis, and ML concepts to real-world aerospace/defense challenges. You’ll receive mentorship, collaborate with engineers, and present findings to senior leadership at rotation end.