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Symbolic Ai Jobs (NOW HIRING)

TekAssembly

Neuro-Symbolic AI Manager

Santa Clara, CA · On-site

Role Overview We are looking for an accomplished Neuro-Symbolic AI Manager to lead advanced research and product initiatives involving hybrid AI systems that combine machine learning with symbolic ...

Intuit

Senior Staff AI Research Scientist

San Diego, CA

$226K - $306K/yr

Conduct original research in decision-focused AI, probabilistic modeling, causal inference, simulation-based planning, agentic and multi-agent systems, neuro-symbolic AI, deep learning, and/or LLM ...

Intuit

Senior Staff AI Research Scientist

San Diego, CA · On-site

$226K - $306K/yr

Conduct original research in decision-focused AI, probabilistic modeling, causal inference, simulation-based planning, agentic and multi-agent systems, neuro-symbolic AI, deep learning, and/or LLM ...

Intuit

Staff AI Research Scientist

Mountain View, CA · On-site

$209K - $283K/yr

... symbolic AI, fundamentals of deep learning architectures and model training (pre and post) , and reinforcement learning, and LLM based reasoning for real-world business decision workflows. You will ...

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Symbolic Ai information

What is symbolic AI?

Symbolic AI is a branch of artificial intelligence that uses high-level, human-readable symbols and logic to represent knowledge and solve problems. It relies on rules, ontologies, and reasoning engines to mimic human decision-making and understanding. Unlike machine learning methods, which learn patterns from data, symbolic AI systems are programmed with explicit rules and relationships. This approach is particularly useful for tasks that require explainability and reasoning, such as expert systems and knowledge graphs.

What skills and qualifications are needed to thrive as a symbolic AI engineer?

To excel as a Symbolic AI Engineer, you need a solid background in computer science, logic, and mathematics, often supported by a degree in a related field. Experience with knowledge representation languages (like OWL or Prolog), logic programming, and relevant AI frameworks is highly valuable. Analytical thinking, problem-solving, and effective communication are crucial soft skills for designing and explaining complex reasoning systems. These skills ensure the development of robust, interpretable AI solutions that can tackle intricate, rule-based problems in various industries.

What are common challenges faced by professionals working in symbolic AI roles, and how can they be addressed?

Professionals in Symbolic AI roles often encounter challenges related to integrating symbolic reasoning with machine learning approaches, as well as handling the complexity of knowledge representation. Collaborating effectively with data scientists and software engineers is crucial, as is staying up-to-date with developments in hybrid AI systems. Regularly participating in interdisciplinary meetings and knowledge-sharing sessions can help address these challenges and enhance project outcomes.

What is the difference between Symbolic Ai vs Data Scientist?

AspectSymbolic AiData Scientist
Required CredentialsComputer Science, AI, or related degrees; knowledge of logic and knowledge representationStatistics, Mathematics, Computer Science; proficiency in programming and data analysis
Work EnvironmentResearch labs, AI development teams, academiaTech companies, finance, healthcare, consulting firms
Industry UsageKnowledge-based systems, expert systems, reasoning enginesData analysis, predictive modeling, machine learning applications

Symbolic AI focuses on rule-based reasoning and knowledge representation, often used in expert systems. Data Scientists analyze data to build models and extract insights. While both roles involve AI, Symbolic AI emphasizes logic and knowledge structures, whereas Data Scientists focus on data-driven modeling and statistical analysis.

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Infographic showing various Symbolic Ai job openings in the United States as of September 2026, with employment types broken down into 86% Full Time, and 14% Contract. Highlights an 71% In-person, and 29% Remote job distribution.

Neuro-Symbolic AI Manager

Santa Clara, CA • On-site

TekAssembly
See more jobs at TekAssemblyexternal

Other

Posted 13 days ago

Job description

Role Overview

We are looking for an accomplished Neuro-Symbolic AI Manager to lead advanced research and product initiatives involving hybrid AI systems that combine machine learning with symbolic reasoning. The role will focus on designing innovative AI algorithms, leading multidisciplinary teams, and taking research concepts through scalable production deployment.

Key Responsibilities

    Define and drive the roadmap for Neuro-Symbolic AI, reasoning, and hybrid AI systems.

    Lead AI initiatives from research and algorithm design through production deployment.

    Architect and optimize neuro-symbolic AI models for large-scale, real-world applications.

    Develop hybrid architectures combining deep learning, symbolic reasoning, knowledge representation, and probabilistic logic.

    Lead and mentor AI researchers, ML engineers, and data scientists.

    Collaborate with research teams, academic institutions, and technology partners.

    Drive technical innovation and contribute to research publications, patents, and advanced AI initiatives.

    Ensure AI solutions are scalable, explainable, secure, and production-ready.

Required Skills & Experience

    15+ years of experience in AI research, machine learning, software engineering, or applied AI.

    PhD required in Computer Science, Artificial Intelligence, Data Science, or a closely related discipline.

    Advanced Python and Go programming experience.

    Strong hands-on expertise with PyTorch, PyG (PyTorch Geometric), and PyKEEN.

    Deep understanding of Neuro-Symbolic AI, symbolic reasoning, knowledge representation, and hybrid AI architectures.

    Strong experience with Graph Neural Networks (GNNs), knowledge graphs, probabilistic logic, and automated reasoning.

    Experience with Explainable AI (XAI) and interpretable AI systems.

    Hands-on experience deploying AI/ML systems on AWS or Azure.

    Experience designing scalable microservices architectures for AI/ML solutions.

    Strong leadership experience managing or mentoring multidisciplinary AI/ML teams.

Preferred Background

    Experience with AI research labs, hyperscale technology companies, or advanced AI organizations.

    Experience in healthcare, financial services, retail, robotics, autonomous systems, or other knowledge-intensive domains.

    Experience with reinforcement learning, multimodal AI, LLMs, AI agents, and knowledge graphs.

    Experience taking research prototypes into enterprise-scale production environments.

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