Job Description
Company Description
Daice Labs is building hybrid AI frameworks that integrate today's models into systems that learn continuously. Founded by MIT CSAIL scientists, we focus on building new architectures by combining LLMs/DL with symbolic reasoning and bio-inspired system design. Operating on two tracks, our Product Lab develops industry-specific solutions for collaborative human teams + AI co-building and co-owning vertical applications, while our Research Lab explores how principles of natural intelligence can guide systems design of new hybrid AI architectures.
Join us in taking the next leap in productivity through collaborative innovation.
Role Description
This is a full-time remote role for an AI Engineer Intern. The intern will be responsible for assisting in the research and development of hybrid AI systems, focusing on pattern recognition, neural networks, natural language processing (NLP), and software development. Day-to-day tasks include implementing and testing AI models, conducting research on AI frameworks, and collaborating with the team to develop innovative AI solutions.
Qualifications
- Pattern Recognition and Neural Networks skills
- Strong background in Computer Science and Software Development
- Experience with Natural Language Processing (NLP)
- Proficiency in machine learning (ML) Algorithms (proficiency in python and ML-stack languages)
- Excellent problem-solving skills and ability to work independently
- Strong communication and collaboration skills
- Interest in hybrid AI frameworks
- Currently pursuing or recently completed a degree in Computer Science, Engineering, or a related field. Being a PhD student is a plus.
- Experience building ML platforms/dev‑tools; you’ve developed agentic systems, sandboxes, or orchestration systems
- Strong in TypeScript + Python; API design, JSON Schema
- Experience in Containers and quotas, Observability, Postgres, Redis.
- Experience in Evaluation; you’ve built golden sets, regressions, drift detection, and gates for ML/agents
- Experience in Security & privacy chops (network allow‑lists and auditability)