Job Title: Neuro-Symbolic AI Manager
Location: Redmond, WA (initial Remote can be discussed during interview)
Job Type: Contract (12+ Months, likely extension)

POSITION OVERVIEW

We are seeking few accomplished Neuro-Symbolic AI Manager to lead advanced research and productization efforts in hybrid AI systems that combine machine learning with symbolic reasoning. This role blends deep technical leadership with hands-on algorithm design, working at the forefront of explainable and high-performance AI solutions.

REQUIRED QUALIFICATIONS

• Education: PhD in Computer Science, Artificial Intelligence, Data Science, or closely related field.
• Experience: Minimum 15 years in AI research, software development, and applied neuro-symbolic AI.
• Programming Expertise: Advanced proficiency in Python and Go, with hands-on experience using neuro-symbolic AI toolkits such as PyG, PyKEEN, and PyTorch.
• Domain Knowledge: Deep understanding of knowledge representation, symbolic reasoning, and machine learning.
• Strong background in graph neural networks, probabilistic logic, and hybrid AI architectures.
• Leadership & Soft Skills: Proven track record leading large-scale AI initiatives, with exceptional communication and cross-functional collaboration skills.
• Hands-on experience deploying AI systems on Azure.
• Familiarity with automated reasoning systems, logic programming, and explainable AI (XAI) principles.
  Experience with scalable microservices architectures for AI solutions.
  Job Type: Contract
  Pay: $70.00 - $90.00 per hour
  Work Location: In perso

1. Technical Leadership & Strategy

• Define and drive the organization’s neuro-symbolic AI roadmap in alignment with business objectives.
• Lead AI-driven initiatives from research concept through scalable production deployment.
• Mentor and guide multidisciplinary teams of AI researchers, engineers, and data scientists.

1. Algorithm Design & Implementation

• Architect, develop, and optimize neuro-symbolic AI algorithms for mission-critical, real-world use cases.
• Implement and evaluate hybrid AI models using large, real-world datasets.
• Ensure efficient, clean, and well-documented code following industry best practices.

1. Research & Innovation

• Collaborate with global research teams, academic institutions, and industry partners to advance neuro-symbolic AI methodologies.
• Publish and present technical findings at top AI conferences and journals.
• Lead technical ideation to enhance and evolve existing AI solutions.