OVERVIEW:

We are seeking a highly motivated Machine Learning / Deep Learning Specialist to implement AI algorithms across a variety of data types—including images, video, audio, and text—to support research initiatives, demonstrations, and academic collaborations. The ideal candidate will not only be technically proficient but also capable of clearly communicating their work through well-crafted PowerPoint presentations and documentation. This role involves collaboration with faculty, researchers, and students, with an emphasis on knowledge-sharing and training others in practical machine learning workflows.
This position is ideal for undergraduate students, graduate students, or working professionals with hands-on experience in AI/ML who are interested in contributing to academic research while mentoring others.
U.S. Citizenship is required for this position.

PREFERRED QUALIFICATIONS:

· Current undergraduate, graduate student, or working professional with practical experience in machine learning and deep learning.
· Proficiency with Python and popular AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn, Hugging Face, etc.
· Experience working with various data modalities (image, video, audio, text).
· Strong data processing and visualization skills.
· Ability to clearly communicate technical concepts to diverse audiences through documentation and presentations.
· Experience working in collaborative research environments with faculty and students.
· Previous experience in teaching, tutoring, or mentoring in technical subjects is a strong plus.
· Familiarity with Jupyter Notebooks, Git, and cloud-based AI platforms is desirable.

SOFT SKILLS:

· Strong interpersonal and communication skills.
· Comfortable engaging with college professors, researchers, and students.
· Ability to explain complex concepts in an accessible way.
· Collaborative mindset with a willingness to share knowledge.

· Implement and customize machine learning and deep learning algorithms on diverse datasets (images, video, audio, text, etc.).
· Develop scripts, notebooks, and workflows that facilitate experimentation and reproducibility.
· Create clear and concise PowerPoint presentations and documentation to describe methodologies, results, and lessons learned.
· Collaborate closely with college professors, researchers, and students to align AI solutions with research goals.
· Conduct hands-on training sessions and workshops to help students and faculty gain practical ML/DL skills.
· Stay informed on the latest developments in AI/ML and recommend technologies and approaches that could benefit ongoing projects.
· Support live demonstrations and capability briefs of AI solutions to both technical and non-technical audiences.