Principal AI/ML Researcher – Reasoning, Planning, Decision-making Systems

Airbnb

Principal AI/ML Researcher at Airbnb focusing on reasoning, planning, and intelligent decision-making systems. Collaborate across disciplines and influence company-wide AI architecture.

Posted 6/1/2026full-timeRemote • 🇺🇸 United StatesLead💰 $296,000 - $370,000 per yearWebsite

Tech Stack

Tools & technologies

JavaPythonPyTorchRay

About the role

Key responsibilities & impact

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.
Ensure modularity and extensibility through multi-agent frameworks, agentic substrates, and declarative planning pipelines.
Define communication protocols, coordination strategies, and cross-agent knowledge alignment mechanisms to foster emergent cooperative intelligence.

Requirements

What you’ll need

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

Comp & perks

This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

JavaPythonC++Reinforcement LearningMachine LearningPost-training architecturesPlan inductionValue estimationContingency modelingMulti-agent systems

Soft Skills

Communication protocolsCoordination strategiesKnowledge alignmentCooperative intelligence

Certifications

Masters in Computer ScienceMasters in AIMasters in Cognitive Science