Principal AI/ML Researcher / Engineer In Reasoning, Planning, and Decision-making systems - Careers

Airbnb

Remote

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Work type
Remote
Location
Remote
Posted
3 days ago

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Similar $216k
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About Airbnb

Founded in 2008 and formerly known as AirBed & Breakfast, Inc., Airbnb is a global marketplace connecting travelers with hosts who offer unique accommodations, ranging from private rooms to entire homes. It operates a massive digital platform for booking stays, experiences, and travel services worldwide.

Airbnb currently has 74 open roles on FindRole.

Listed pay typically runs $204,000–$255,000 across 44 roles with salary data.

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TL;DR · Principal AI/ML Researcher / Engineer In Reasoning, Planning, and Decision-making systems - Careers

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As a Principal/Distinguished AI/ML Researcher and Engineer at Airbnb, you will join a cutting-edge team to develop cognitive AI systems that integrate large reasoning models with knowledge graphs and reinforcement learning. Your daily responsibilities include driving research in decision-making frameworks, designing scalable multi-agent systems, and developing hybrid model architectures that combine symbolic and sub-symbolic methods. You will work with Python, PyTorch, Ray, JAX, and RLlib to build intelligent decisioning substrates for personalized environments, focusing on improving decision quality and operational coordination across guest and host workflows. This role requires extensive experience in AI/ML, particularly in post-training architectures and production-scale reasoning systems, as well as a deep understanding of reinforcement learning and multi-agent coordination.

Skills

Python PyTorch Ray JAX RLlib Reinforcement Learning Knowledge Graphs Large Language Models Multi-Agent Systems Distributed RL Symbolic-Connectionist Integration Causal Modeling Recursive Task Planners Hybrid Model Architectures Post-Training Architectures CI/CD

What you'll do

  • Drive foundational and applied research in reasoning engines, planning architectures, and decision-making frameworks at scale.
  • Design methods for plan induction, value estimation, and contingency modeling within intelligent agents.
  • Architect RPD systems integrating post-trained LLMs/LRMs with graph-structured memory and RL-driven controllers.
  • Define communication protocols and coordination strategies for emergent cooperative intelligence in multi-agent systems.
  • Develop stateful models combining supervised learning with online/offline reinforcement and simulation-based rollouts.

What we're looking for

  • Extensive experience (15+ years) in AI/ML, including post-training architectures and production-scale reasoning systems.
  • Advanced coding proficiency in Java, Python, C++, or similar, with extensive use of ML/RL frameworks at scale.
  • Proven track record integrating LLMs/LRMs with Knowledge Graphs or structured world models.
  • Deep expertise in Reinforcement Learning for decisioning and planning applications.
  • Fluency in hybrid model architectures combining connectionist-symbolic fusion, retrieval-based agents, or goal-directed transformers.
  • Experience leading multi-agent coordination, distributed RL, or cooperative inference systems.

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