What are the responsibilities and job description for the Applied AI engineer intern position at Daice Labs?
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 AI Engineer Intern will work on developing and refining algorithms, neural networks, and other AI models. Day-to-day tasks include pattern recognition, conducting research on AI principles, and collaborating with the team on continuous learning systems. This role offers a hands-on experience in both practical product development and advanced AI research.
Qualifications
- Strong understanding of Pattern Recognition, Neural Networks, LLMs, agentic architectures
- Proficiency in Computer Science and ML Algorithms (proficiency in python and ML stack languages)
- Experience with Statistics
- Good problem-solving skills and the ability to work collaboratively
- Excellent written and verbal communication skills
- Bachelor's degree in AI, Machine Learning, Computer Science, or related field
- Experience in bio-inspired system design and hybrid AI architectures is a plus
- Ability to work independently and remotely
- Experience building ML platforms/dev‑tools; you’ve shipped SDKs, sandboxes, or orchestration systems
- Strong in TypeScript Python (Go a plus); API design (gRPC/REST), JSON Schema, signing/SBOMs
- Containers/WASM, quotas, least‑privilege patterns
- Observability (OTel), Postgres/pgvector, Redis; message queues (SQS/NATS/Kafka)
- Evaluation mindset; you’ve built golden sets, regressions, drift detection, and CI gates for ML/agents
- Security & privacy chops (network allow‑lists, PII controls, auditability)