Skills

Communication Python Problem-solving Research Effective Communication Machine Learning Programming

Job Specifications

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 a Junior AI/ML Engineer in our R&D department. The Junior AI/ML Engineer will be responsible for developing and implementing AI and machine learning algorithms, conducting research on new AI methodologies, and applying statistical models to improve hybrid AI systems. Day-to-day tasks include analyzing data , building hybrid architectures (LLMs/DL, symbolic reasoning), and designing efficient evaluation benchmarks. Collaboration with cross-functional teams to integrate AI solutions into broader projects is also a key aspect of this role.

Qualifications

Strong foundation in Computer Science
Strong foundation in Machine learning algorithms, LLMs, agentic architectures
Proficiency in Statistics
Proficiency in python and ML stack programming languages/libraries/toolkits
Excellent problem-solving skills and ability to work independently
Effective communication skills for collaborative work
Bachelor's or Master's degree in Computer Science, AI, or related field
Experience with hybrid AI system design is a plus

About the Company

Daice Labs -- Digital co-builders engineering adaptation The Problem Current AI shines on benchmarks but breaks in the wild--brittle under distribution shift, costly to retrain, hard to audit, and slow to adapt. The need? new hybrid paradigms that integrate today's models into systems that learn continuously, generalize, and explain their decisions. Approach Founded by MIT CSAIL scientists, we build hybrid/composite frameworks that combine LLMs/DL with symbolic reasoning and bio-inspired system design to deliver new architec... Know more