Principal AI/ML Researcher / Engineer, Airbnb

• 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

Desirable

• 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
• 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
• 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
• 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
• 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
• 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
• 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