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Benzinga

Machine Learning Intern (Compensated)
Location: Remote/Anywhere
Timings: Asia Day Hours, Monday to Friday
Job Summary:
Benzinga is excited to offer an opportunity for a Machine Learning Intern to be a
part of our innovative team. This role is ideal for individuals who are passionate
about artificial general intelligence (AGI), machine learning, and — ideally — the
world of finance. The intern will work closely with our content team to create new
story types using AGI systems and assist in data preparation for the fine-tuning of
these systems.
Responsibilities:
Collaborate with the content team to develop and implement new story types
using AGI systems.
Assist in the collection, cleaning, and preparation of JSONL datasets for AGI
training and fine-tuning.
Participate in the development and refinement of prompts for AGI systems to
generate engaging and coherent stories.
Contribute to the understanding of AGI behavior and outputs, providing insights
and feedback to improve story quality and variety.
Stay updated on the latest developments in AGI, machine learning, and related
technologies.
Participate in regular team meetings and provide updates on project progress.
Requirements:
● Currently pursuing or recently completed a Bachelor’s or Master’s degree in
Computer Science, Machine Learning, Artificial Intelligence, or a related
field.
● Basic understanding of machine learning concepts and experience with
Python -programming language.
● Interest in storytelling, content creation, and the intersection of technology
and media.
● Strong analytical skills and attention to detail.
● Ability to work collaboratively in a fast-paced, dynamic environment.
● Excellent communication skills and the ability to present complex technical
information clearly.
● Proactive and eager to learn and contribute to team goals.
How To Apply:
Interested candidates can apply using LinkedIn or email their resume and cover
letter to neer @ benzinga.com. Please include a casual cover letter and any relevant
projects or coursework related to machine learning or AGI systems.