Machine Learning R&D Co-Op

In This Role, Your Responsibilities Will Be:

• Participate in the architecture of data collection, organization, and analysis from various sources including manufacturing and global service partners for AI/ML based diagnostics and prognostics R&D.
• Design, plan, and conduct lab experiments to generate high-quality data for evaluating, validating, and improving AI/ML models for diagnostics and prognostics research.
• Work with R&D and data science teams to perform exploration data analysis, identifying trends and features.
• Help plan & roadmap new features and functionality.
• Work in a multi-disciplinary agile team supporting research, data science, and prognostics teams in developing and improving AI/ML models.
• Document and present progress/results to the data science and prognostics teams as well as other stakeholders.
• Assist with other project activities as assigned.

Who You Are:

• You are self-motivated. You balance planning with actions. You solicit both input and discussion. You focus on priorities and set stretch goals.

For This Role, You Will Need:

• Enrollment in a Bachelor's degree program in Mechanical Engineering, Electrical Engineering, Computer Engineering, Computer Science, Data Science, Mathematics, Statistics, or a related engineering/science field.
• Experience with Python, Matlab, LabView or similar technical computing language.
• Ability to work in a laboratory environment and follow structured experimental procedures.
• Strong analytical, problem-solving, documentation, and communication skills.

Preferred Qualifications That Set You Apart:

• Junior or higher class standing

• Cumulative GPA of 2.5 or higher

• Experience with Python-based data science and machine learning tools such as NumPy, pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or similar libraries.

• Familiarity with time-series data, sensor data, signal processing, filtering, feature extraction, or frequency-domain analysis.

• Exposure to machine learning methods such as classification, regression, anomaly detection, clustering, neural networks, or probabilistic models.

• Experience designing or supporting lab experiments, test plans, data acquisition, or Design of Experiments.

• Interest in diagnostics, prognostics, predictive maintenance, reliability engineering, or condition-based monitoring.

• Ability to connect experimental observations with physical system behavior and communicate findings clearly to both technical and non-technical audiences.

• Experience with data visualization, technical reporting, dashboards, or database/storage tools is a plus.

Our Culture & Commitment to You: