What are the responsibilities and job description for the AI/ML intern position at Daice Labs?
Company Description
Daice Labs, founded by MIT CSAIL scientists, tackles the limitations of current AI by designing hybrid systems that generalize, adapt, and explain their decisions. Through integrating LLMs/DL, symbolic reasoning, and bio-inspired designs, Daice Labs creates auditable and adaptive AI frameworks. Our innovative "Digital Cells" approach supports modular, guided program synthesis and hybrid reasoning to power state-of-the-art systems. The company's flagship product, Cofabric, serves as an operating system for collaborative, long-horizon AI-human projects, advancing productivity through shared context and governance. At Daice Labs, we believe the future lies in co-creation and hybrid AI systems.
Role Description
This is a full-time, remote role for an AI/ML Intern at Daice Labs. The AI/ML Intern will collaborate with our research and engineering teams to design, develop, and test innovative hybrid AI models. Responsibilities include conducting data analysis, implementing machine learning and natural language processing (NLP) algorithms, building and optimizing models, and contributing to the development of our "Digital Cells" and Cofabric platform. This role offers an opportunity to tackle groundbreaking challenges in hybrid AI while working in a dynamic and innovative team environment.
Qualifications
- Proficiency in foundational AI/ML concepts, including supervised and unsupervised learning, reinforcement learning, and neural networks
- Experience with hands-on programming and development in Python, along with familiarity with AI/ML libraries like TensorFlow or PyTorch
- Knowledge of natural language processing (NLP) techniques, symbolic reasoning, or hybrid AI systems
- Strong problem-solving, analytical, and research skills with the ability to adapt to novel challenges
- Understanding of data processing, data analysis, and algorithms optimization
- Preferred skills: Experience with AI system auditing, reinforcement learning, evolutionary search, symbolic techniques, and guided program synthesis
- Currently pursuing or recently completed a degree in computer science, artificial intelligence, machine learning, or a related field