Junior AI Engineer

Daice Labs, founded by MIT CSAIL scientists, is devoted to developing hybrid AI systems that are auditable, governable, and adaptive. We aim to solve real-world challenges with frontier AI technology by integrating cutting-edge models into systems that improve over time, generalize reliably, and explain decisions. Our platform enables human teams to co-build and co-own innovative, long-term projects with tools for collaboration, governance, and system-level adaptability. By combining research and product innovation, we deliver solutions for accelerated discovery and innovation in diverse domains. At Daice Labs, we are reimagining productivity and ownership to pave the way for the next leap in collaboration and AI innovation.

We are seeking a full-time Junior AI Engineer to join our team remotely. In this role, you will contribute to developing and improving hybrid AI systems, designing solutions that integrate state-of-the-art machine learning models with adaptive frameworks. Responsibilities include building, testing, and optimizing AI models, developing robust software solutions, and collaborating with cross-functional teams. You will also work on projects involving pattern recognition, neural networks, and natural language processing (NLP) to enhance the capabilities of AI-driven systems.

• A strong foundation in Computer Science concepts and Software Development principles.
• Experience with Pattern Recognition and Neural Networks.
• Knowledge of Natural Language Processing (NLP) and its applications in AI.

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  Proficiency with coding in at least one programming language, such as Python, Java, or C++.
• Ability to contribute to innovative AI-driven projects and solve real-world challenges through research.
• Strong problem-solving skills and the ability to work collaboratively with cross-functional teams.
• Familiarity with AI ethics, governance, and adaptive systems is a plus.
• Degree in Computer Science, Artificial Intelligence, or a related field; a focus on AI or machine learning is preferred.