Machine Learning Engineer Co-Op

Description

Brief Description of Role:

If you enjoy designing innovative systems and solving real-world problems, you'll have fun working with us! Co-ops at Etegent get the opportunity to work on projects that make a difference in a wide range of exciting fields. Etegent conducts cutting edge research in areas such as:

• Training machine learning models to find objects in radar, lidar, panchromatic, hyperspectral and vibrometry data.
• Developing tools to reveal hidden insights in massive, diverse sets of data.
• Curating synthetic and measured data in support of algorithm development.
• Developing performance models for autonomous systems.
• High-performance computing – from low swap to supercomputers.

Etegent has a long history of working with co-students to help them achieve their professional goals. In fact, many of our full-time engineers began their careers with Etegent as co-ops. We know first-hand the importance of providing co-ops with high quality learning experiences tailored to the individual’s goals.

Responsibilities:

At Etegent, co-op students get the opportunity to work closely with our researchers to help develop and implement cutting edge technology. The specific tasks will vary based on the project and the individual’s skills, interests, knowledge, and aptitude, but will involve developing innovative solutions to solve customer problems.

Requirements

Minimum Qualifications:

(Our expectations will vary based on the education level of the applicant.)

• Exposure to Python and Git.
• Exposure to data structures and program design.
• Comfortable working with Linux operating systems.
• Ability to travel to our Beavercreek office at least 3 times a week.

Preferred Qualifications:

(Our expectations will vary based on the education level of the applicant.)

• Exposure to ML/AI frameworks (e.g., PyTorch, TensorFlow).
• Exposure to Python statistical/scientific computing packages (e.g., NumPy, Scikit-Learn).
• Exposure to Computer Vision concepts and terminology.
• Visual interface experience (e.g., plotly, application servers, web design).
• Statistical pattern recognition.