Job Description:

• Drive foundational and applied research in reasoning engines, planning architectures, and decision-making frameworks at scale.
• Advance techniques in LLM/LRM post-training, reinforcement learning–based decisioning, and knowledge-integrated agents.
• Design methods for plan induction, value estimation, and contingency modeling within intelligent agents.
• Explore and validate protocols for distributed reasoning and joint planning among cooperative agents in multi-agent systems.
• Architect RPD systems that integrate post-trained LLMs/LRMs, graph-structured memory (e.g., KGs), and RL-driven controllers.
• Design recursive task planners, search-based or policy-based reasoners, and belief-state trackers that can interoperate with large model substrates.
• Build and evolve stateful, dynamic models that combine supervised learning with online/offline reinforcement, simulation-based rollouts, and symbol grounding.
• Set direction for planning/reasoning infrastructure within the AI/ML platform strategy.

Requirements:

• Masters or equivalent in Computer Science, AI, Cognitive Science, or related fields.
• Recent published work or patents in AI, Cognitive Science, or related fields.
• 15+ years in AI/ML, including post-training architectures and production-scale reasoning systems.
• Advanced coding proficiency in Java, Python, C++, or similar, with experience in ML/RL frameworks (e.g., PyTorch, Ray, JAX, RLlib) at scale.
• Proven experience integrating LLMs/LRMs with Knowledge Graphs or structured world models.
• Deep understanding of Reinforcement Learning and its application to decisioning and planning.
• Fluency in hybrid model architectures: connectionist-symbolic fusion, retrieval-based agents, or goal-directed transformers.
• Experience working on multi-agent coordination, distributed RL, or cooperative inference systems.

Benefits:

• Bonus
• Equity
• Employee Travel Credits