Principal AI/ML Researcher / Engineer (In Reasoning, Planning, and Decision-Making Systems)

Role overview

You will lead next-generation AI/ML research and engineering focused on reasoning, planning, and decision-making systems. The work includes designing and deploying multi-agent AI architectures and influencing company-wide AI design and deployment.

Key missions

• Invent, scale, and operationalize intelligent decisioning substrates combining symbolic and sub-symbolic approaches.
• Drive foundational and applied research in reasoning engines, planning architectures, and decision-making frameworks—integrating generative AI into business applications.
• Lead development of multi-agent reasoning systems, including:
  • inter-agent knowledge exchange principles
  • goal delegation mechanisms
  • cooperative decision resolution

Requirements

• Deep understanding of Reinforcement Learning (RL) for decisioning and planning.
• Proven experience integrating LLMs/LRMs with Knowledge Graphs and/or structured world models.
• Recent published work or patents in AI, cognitive science, or related areas.
• Fluency in hybrid model architectures, such as:
  • connectionist–symbolic fusion
  • retrieval-based agents
  • goal-directed transformers
• Advanced coding skills in Java, Python, C++, or similar and experience with ML/RL frameworks at scale (e.g., PyTorch, Ray, JAX, RLlib).
• 15+ years AI/ML experience, including post-training architectures and production-scale reasoning systems.
• Master’s (or equivalent) in Computer Science, AI, Cognitive Science, or related fields.
• Experience with multi-agent coordination, distributed RL, or cooperative inference.
• Experience with distributed intelligence platforms featuring explicit agent interaction models and collective decision-making logic.
• Experience with memory architectures, task graphs, or semantic program induction.
• Ability to lead research-to-production transitions cross-functionally.
• Experience in cognitive architectures, neuro-symbolic systems, or agent-based simulation environments.
• Published work/patents in multi-agent reasoning, plan synthesis, knowledge-augmented learning, or generative control.
• Ph.D. in AI/ML/Robotics/Cognitive Systems or related areas.

Nice-to-haves

• Strong background in generative control and/or knowledge-augmented learning.
• Prior contributions to agent-based simulation environments and neuro-symbolic systems.