Responsibilities

• Pioneer the future of decision intelligence by blending symbolic AI with large language models.
• Innovate at the cutting edge of neurosymbolic integration, architecting advanced AI systems that deliver explainability, determinism, and accuracy.
• Collaborate with leading experts to shape technology that transforms complex decision-making.
• Integrate probabilistic and symbolic AI, bridging the gap between pattern-matching capabilities of LLMs and logical precision of symbolic reasoning systems.
• Design systems where LLMs communicate with knowledge graph infrastructure, translating natural language into structured symbolic representations and vice versa.
• Extend platform capabilities by developing algorithms and solutions for interaction between different AI paradigms.
• Create mechanisms for knowledge extraction and transfer between LLMs and symbolic systems.
• Design and implement neurosymbolic architectures that preserve interpretability advantages of symbolic AI while incorporating flexibility of neural networks.
• Explore emerging technologies and methodologies to enhance Rainbird’s capabilities in automated knowledge acquisition, reasoning transparency, and computational efficiency.
• Stay current with academic research and industry developments to identify opportunities for platform evolution.
• Focus on proof-of-concept implementations and technical prototypes to demonstrate feasibility and value.
• Provide technical guidance during the transition of innovations into production-ready features.
• Establish rigorous evaluation frameworks to assess reasoning capabilities of hybrid systems, including designing benchmark tests and creating metrics.

Requirements

• An advanced degree (Master’s or Ph.D.) in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Proficiency in programming languages, particularly Python, and experience with a strongly typed language such as Go.
• Strong understanding of AI, machine learning, or computational reasoning, with hands-on experience in symbolic AI techniques, knowledge representation, and rule-based systems.
• Experience with large language models (LLMs) and associated tooling (e.g., OpenAI, Anthropic, Huggingface).
• Knowledge of vector databases, embeddings, and retrieval-augmented generation style architectures.
• Proven track record of conducting rigorous research and translating theoretical findings into practical solutions.
• Exceptional analytical and problem-solving skills, with a passion for driving innovation within the AI landscape.

Preferred Experience

• Experience in creating integrated AI systems that blend symbolic and neural methodologies.
• Exposure to graph-based reasoning and the construction and utilization of knowledge graphs.
• Background in research and development-focused roles or active participation in academic-industry collaborations.

Why Join Us?

• Work at the cutting edge of AI innovation, pioneering neuro-symbolic intelligence.
• Collaborate with an ambitious and highly skilled team.
• Fully remote work with flexible arrangements.
• Opportunity to contribute to groundbreaking advancements in explainable AI.

Interested candidates should apply below and submit their CV and a brief covering letter outlining relevant experience. We look forward to hearing from you!