Live opening · Posted 2 days ago
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About the role
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Company Description Dynava is an emerging organization exploring the potential of generative AI to build practical, user-centered solutions. It focuses on applying cutting-edge machine learning and large language models to real-world problems across a variety of domains. Team members collaborate in a flexible, innovation-driven environment that values curiosity, experimentation, and continuous learning. As a remote-friendly workplace, the company encourages autonomy, accountability, and open communication among distributed team members.
Role Description This part-time remote Generative AI intern role involves supporting the development, testing, and refinement of AI-powered features and prototypes. Day-to-day tasks may include experimenting with large language models, generating and evaluating model outputs, and helping design prompts and workflows for specific use cases. The intern will assist in preparing datasets, documenting experiments, and summarizing findings for the product and engineering teams. The role also includes researching recent developments in generative AI, contributing to internal knowledge resources, and collaborating with mentors to improve model performance and user experience.
Qualifications
Ability to work with generative AI tools and large language models, including prompt design and results evaluation.
Foundational understanding of machine learning, natural language processing, or data science concepts.
Strong analytical, research, and problem-solving skills to explore use cases and interpret experiment results.
Clear written communication skills for documenting workflows, drafting instructions, and summarizing insights.
Comfort with remote collaboration tools and the ability to manage time effectively in a part-time setting.
Preferred: Experience with Python or similar languages, familiarity with AI/ML libraries or APIs, and coursework in computer science, engineering, or related fields.
Curiosity about emerging AI technologies and willingness to learn in a fast-evolving environment.
Work arrangement
No
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