Principle Engineer -In Bayesian, Large Foundational Systems, and Distributional Reinforcement Learning

About the role

We are seeking a seasoned Principal AI/ML Researcher and Engineer with deep expertise in Bayesian Learning , and Distributional Reinforcement Learning (RL) to lead the advanced research and development of cutting-edge intelligence AI models. These systems will integrate foundational Bayesian frameworks with advanced architectures, including Mixture of Models , multi-pass sharded systems , multitask and multi-objective optimization, and external knowledge incorporation. Additionally, the role involves innovating ways to interoperate and integrate Large Language Models (LLMs) and Large Multimodal Models (LMMs) with Reasoning, Planning, and Decisioning abilities into the Bayesian frameworks to create a seamless foundational model fabric that synergizes with diverse model ecosystems.The role will require ensuring these models and supporting systems perform efficiently at scale , integrating them into live systems that directly impact product and user experience . Our goal is to build next-generation AI platforms that redefine personalization, decision-making, and intelligence across diverse applications. You will work on developing production-level systems, collaborate with cross-functional teams, and play a pivotal role in shaping our AI/ML strategy. Relevance and Impact of This Role This role has the potential to fundamentally transform Airbnb's AI stack from primarily deterministic prediction systems into probabilistic, adaptive, uncertainty-aware intelligence systems capable of reasoning under ambiguity and continuously learning from dynamic environments. In the short term, the impact comes from improving personalization quality, ranking robustness, uncertainty estimation, exploration strategies, and adaptive decision-making across guest and host experiences. Bayesian and reinforcement learning systems would enable Airbnb to move beyond static optimization toward probabilistic and policy-driven intelligence capable of handling sparse data, cold-start problems, long-tail discovery, evolving preferences, and uncertain marketplace dynamics. Guests would receive more adaptive and exploratory recommendations, while hosts and internal systems would benefit from improved forecasting, dynamic optimization, risk-aware decisioning, and more resilient personalization systems. In the medium term, Airbnb could evolve into a deeply adaptive learning ecosystem where foundational models, reinforcement learning systems, probabilistic reasoning frameworks, and multi-agent intelligence continuously coordinate to optimize long-term marketplace outcomes. Instead of isolated models making independent predictions, the platform would increasingly operate through Bayesian intelligence fabrics, reinforcement-driven optimization systems, and uncertainty-aware decisioning architectures that dynamically learn from behavioral feedback, marketplace conditions, and evolving user intent. This would significantly strengthen long-tail discovery, adaptive exploration, cross-domain personalization, and marketplace resilience while enabling more intelligent balancing between user satisfaction, ecosystem health, supply-demand dynamics, and business growth objectives. In the long term, this role helps establish Airbnb's strategic leadership in adaptive probabilistic intelligence and continuously learning AI ecosystems. The systems developed under this role become the foundational intelligence substrate connecting Bayesian learning, reinforcement learning, foundational models, reasoning systems, multi-agent orchestration, and large-scale personalization into a unified adaptive architecture. Airbnb would evolve beyond a platform that merely predicts preferences into an intelligent probabilistic ecosystem capable of reasoning under uncertainty, adapting policies dynamically, learning from sparse and evolving signals, and coordinating long-horizon optimization across the entire marketplace. Over time, this could position Airbnb as one of the most advanced real-world adaptive intelligence platforms in the consumer internet - where AI systems continuously balance exploration, exploitation, uncertainty, personalization, and ecosystem optimization in ways that become increasingly difficult for competitors to replicate.

Responsibilities

• Research & Innovation:

Benefits

Additional Information

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.

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