Description:

• Drive foundational and applied research in reasoning engines, planning architectures, and decision-making frameworks at scale.
• Advance 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.
• Architect systems that integrate post-trained LLMs/LRMs, graph-structured memory, and RL-driven controllers.
• Design recursive task planners, search-based or policy-based reasoners, and belief-state trackers.
• Define communication protocols, coordination strategies, and cross-agent knowledge alignment mechanisms for multi-agent systems.
• Build and evolve stateful models that combine supervised learning with online/offline reinforcement, simulation-based rollouts, and symbol grounding.
• Set direction for planning and reasoning infrastructure within the AI/ML platform strategy.
• Work across product, infrastructure, design, researchers, ontologists, and ML engineers to translate ambiguous product intent into multi-stage reasoning pipelines.
• Productionize real-time reasoning loops with low-latency inference, caching, retrieval-augmented generation, and streaming updates to symbolic memory.
• Create monitoring, attribution, and evaluation pipelines for agent behavior and decision quality.

Requirements:

• Master’s degree or equivalent in Computer Science, AI, Cognitive Science, or a related field.
• Recent published work or patents in AI, Cognitive Science, or related fields.
• 15+ years of experience in AI/ML, including post-training architectures and production-scale reasoning systems.
• Advanced coding proficiency in Java, Python, C++, or similar languages.
• Experience with ML/RL frameworks such as PyTorch, Ray, JAX, or 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 such as connectionist-symbolic fusion, retrieval-based agents, or goal-directed transformers.
• Experience working on multi-agent coordination, distributed RL, or cooperative inference systems.
• Ph.D. in AI, Machine Learning, Robotics, Cognitive Systems, or a related area preferred.
• Published work or patents in multi-agent reasoning, plan synthesis, knowledge-augmented learning, or generative control preferred.
• Experience with cognitive architectures, neuro-symbolic systems, or agent-based simulation environments preferred.
• Demonstrated ability to lead cross-functional research-to-production transitions preferred.
• Experience with memory architectures, task graphs, or semantic program induction preferred.
• Prior work on distributed intelligence platforms with explicit agent interaction models and collective decision-making logic preferred.
• Must live in a state where Airbnb, Inc. has a registered entity for this US-remote-eligible role.

Benefits:

• Base pay range of $296,000 to $370,000 USD.
• Eligible for bonus compensation.
• Eligible for equity.
• Eligible for benefits.
• Eligible for Employee Travel Credits.
• US-remote-eligible with occasional office or offsite attendance as agreed with the manager.
• Reasonable accommodations are available for candidates with disabilities during the application and interview process.