About the Role

We are seeking a Principal / Distinguished AI/ML Researcher and/or Engineer with deep experience in reasoning, planning, and decision-making systems. This role is ideal for individuals who have architected post-training intelligence frameworks, integrated Large Reasoning Models (LRMs) with Knowledge Graphs, and applied Reinforcement Learning (RL) as a first-class component of adaptive planning and control. You will be responsible for inventing, scaling, and operationalizing intelligent decisioning substrates that blend symbolic and sub-symbolic methods, enabling next-generation AI systems that go beyond pattern recognition into the realm of deliberation, foresight, and agency.

Our mission is to build cognitive AI systems that combine post-trained foundational models, explicit memory and knowledge, and recursive planning strategies to power sophisticated real-world decisioning in personalized environments. You will collaborate across disciplines and influence company-wide AI architecture.

A core dimension of this role is the design and deployment of multi-agent systems, where reasoning, planning, and decisioning are distributed across networks of intelligent agents. You will formulate coherent, synergistic strategies that enable agents to cooperate, negotiate, and align objectives, ensuring that distributed intelligence converges to purposeful, high-quality outcomes across contexts.

What You Will Do

Research & Innovation

• Drive foundational and applied research in reasoning engines, planning architectures, and decision-making frameworks at scale in order to incorporate genAI into the ranking / recommendation / personalization stack in both single model to multi-agent ( system ) level intelligence with objective to grow the business (new user growth, abandoned user, long tailed user) in existing and new business areas while supporting Multi-Modal NL → Conversational Interfaces.
• 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.

System Design & Architecture

• 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.
  
  

Model Development

• Build and evolve stateful, dynamic models that combine supervised learning with online/offline reinforcement, simulation-based rollouts, and symbol grounding.
• Implement hybrid pipelines that couple learned embeddings, prompted generative models, and graph-theoretic inference.
• Optimize systems for adaptive exploration, planning horizon control, and policy robustness.
• Develop frameworks for distributed value propagation, multi-agent credit assignment, and global planning from local agents.

Technical Leadership

• Set direction for planning/reasoning infrastructure within the AI/ML platform strategy.
• Serve as the technical conscience and architectural leader across high-stakes AI initiatives involving autonomous agents or high-fidelity decision pipelines.
• Mentor teams in systems thinking, causal modeling, symbolic-connectionist integrations, and long-term planning under uncertainty.
• Lead development of multi-agent reasoning systems, defining principles for inter-agent knowledge exchange, goal delegation, and cooperative decision resolution.

Collaboration

• Work across disciplines—product, infra, and design—to translate ambiguous product intent into multi-stage reasoning pipelines.
• Partner with researchers, ontologists, and ML engineers to encode world knowledge, goals, and values into usable inference artifacts.
• Contribute to a company-wide understanding of what it means to make intelligent choices, not just predictions.
• Collaborate with internal teams on distributed agent coordination, shared memory protocols, and policy harmonization across decision surfaces.

Operational Excellence

• Productionize real-time reasoning loops with low-latency inference, caching, retrieval-augmented generation, and streaming updates to symbolic memory.
• Deploy post-training hooks for inserting logic, constraints, and domain priors into existing large models.
• Create advanced monitoring, attribution, and evaluation pipelines for agent behavior and decision quality.
• Operationalize multi-agent orchestration, ensuring reliable and fault-tolerant communication and decision propagation.

Minimum Qualifications

• 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.

Preferred Qualifications

• Ph.D. in AI, Machine Learning, Robotics, Cognitive Systems, or related areas.
• Published work or patents in multi-agent reasoning, plan synthesis, knowledge-augmented learning, or generative control.
• Experience in cognitive architectures, neuro-symbolic systems, or agent-based simulation environments.
• Demonstrated ability to lead cross-functional research-to-production transitions.
• Experience with memory architectures, task graphs, or semantic program induction.
• Prior work on distributed intelligence platforms with explicit agent interaction models and collective decision-making logic.

Your Location:

This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list . If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from.

Our Commitment To Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.

We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process. 

We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application.