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

Job Summary

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.

About the Role

This role advances Airbnb's AI capabilities toward reasoning, planning, and adaptive decision-making across complex real-world environments. The near-term impact spans improved decision quality, contextual intelligence, adaptive personalization, and operational coordination across guest and host workflows — introducing goal-directed reasoning systems capable of handling ambiguity, constraints, trade-offs, and multi-step planning. Guests benefit from more intelligent planning and assistance, while hosts and internal teams gain systems capable of adaptive optimization, dynamic recommendations, policy-aware decisioning, and intelligent workflow orchestration.

Responsibilities

• 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.

Requirements

• 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.
• 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.

Qualifications

• Experience in cognitive architectures, neuro-symbolic systems, or agent-based simulation environments.
• Prior work on distributed intelligence platforms with explicit agent interaction models and collective decision-making logic.

Benefits

• Flexible work arrangements.
• Competitive compensation package.
• Health, dental, and vision insurance.
• Retirement savings plans.
• Employee travel credits.

Pay

$296,000—$370,000 USD

Schedule

Remote Eligible