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Midé is seeking a Software Engineering Intern/Co-op in Woburn, MA to work on sensor data analysis, visualization, and AI-driven engineering tools. You will develop software to test and process real sensor outputs and collaborate with electrical, software, and mechanical teams.
Preferred candidates have strong Python experience with NumPy/SciPy/Pandas, knowledge of signal processing, ML libraries, and data visualization. Familiarity with Linux and cloud platforms is a plus.
Internship - Software Engineering Intern/ Co-op
About Us:
Midé is a sensor and smart materials company located in Woburn, MA that focuses on engineering research (looking for new solutions to problems in commercial, automotive, and defense) and development (implementing new features or products for our enDAQ line of low powered sensors). Engineering has been deep in our culture since we were founded over 30 years ago, and people at all levels of the company have enthusiasm for the technical details of projects. In 2019, we were acquired by Hutchinson, which relies on us for research and problem solving in automotive, aerospace, and wind turbine projects, without interfering with our small company feel.
Midé’s sensor product line, enDAQ, has released and supports multiple open-source Python libraries including EBMLite, for managing EBML encoding schemas, and enDAQ, for interfacing with our sensor recordings and processing data to produce and plot characteristics such as pseudo-velocity shock response spectrums.
Job Description: We are looking for a Software Engineering Intern/Co-op with experience in Python and an interest in sensor data analysis, signal processing, and engineering applications. The intern will work on developing software tools to test, process, analyze, and visualize sensor output, collaborating with a multidisciplinary electrical, software, and mechanical engineering team.
The role offers hands-on experience working with real-world sensor data, applying machine learning and AI techniques to engineering problems, and developing software tools for data analysis and visualization.
Work with acceleration time-series data from vibration sensors, including data visualization, preprocessing, statistical analysis, and feature extraction to identify patterns and derive engineering insights.
Perform frequency-domain and time-frequency analysis, including Fast Fourier Transform (FFT), Power Spectral Density (PSD), filtering, and frequency-based feature extraction.
Develop and apply machine learning models for time-series classification, anomaly detection, and estimation of engineering parameters from sensor measurements.
Use AI tools to accelerate software development, automate workflows, and improve the efficiency of data processing, analysis, and engineering tasks.
Experience programming in Python, particularly with NumPy, SciPy, and Pandas.
Familiarity with signal processing and time-series analysis, including Fourier transforms, FFT, PSD, filtering, statistical analysis, and feature extraction.
Knowledge of machine learning techniques and libraries such as Scikit-learn, PyTorch, or TensorFlow is a plus.
Experience with data visualization and dashboard development, using tools such as Matplotlib, Plotly, Dash, or Streamlit.
Experience using and configuring Linux, particularly Debian.
Familiarity with Django or cloud platforms such as AWS or Microsoft Azure is a plus.
Interest in applying AI tools and LLMs to software development, data analysis, and engineering applications.
Experience working with sensor data, hardware interfaces, or physical systems is beneficial.
Due to the nature of our government work, the applicant must be a U.S. person (citizen or Green Card holder/permanent resident)
Strong academic record in Software Engineering or similar (eg. Physics or Math with appropriate experience).
Desire to contribute to a dynamic environment in a self-motivated, action-oriented fashion.
Ability to work well both as part of team and independently.
Efficient personal time management, organization, and task prioritization
Strong technical communication (verbal and written) to effectively present/report work for internal engineering team and potentially external customers