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

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., * Define and drive the architecture of large-scale Bayesian Framework-based AI systems at Airbnb.

• Develop multi-pass sharded Bayesian + Discriminative/Generative single to multi agent systems for scale and efficiency.
• Incorporate Mixture of Models and Agents, multitask learning, multi-objective optimization, and external knowledge systems into model designs.
• Innovate methods to interoperate with LLMs, LRMs, LMMs, and transformer-based architectures, ensuring seamless integration and collaboration within the AI ecosystem using AI Multi-Agentic Frameworks.

Model Development:

• Build and refine Bayesian or Markovian Graph chains to incorporate uncertainty estimation, adaptive decision-making, and probabilistic reasoning.
• Develop foundational models by merging Bayesian techniques with Classical ML with L[L/M/R]Ms and other advanced architectures, ensuring compatibility and synergy.
• Continuously improve systems for scalability, performance, and robustness, enabling models to absorb and adapt to diverse data sources and paradigms.

Technical Leadership:

• Lead technical direction and strategy for AI/ML systems.
• Influence cross-functional teams, including engineering leaders, product managers, and data scientists, to adopt unified intelligence platform approaches.
• Perform code reviews, mentor engineers, and champion best practices in AI/ML., * Work with structured and unstructured data to design models for diverse use cases.
• Collaborate with cross-functional partners to identify opportunities, refine requirements, and drive impactful solutions.
• Translate complex technical decisions into business value.

Operational Excellence:

• Develop, productionize, and maintain scalable AI/ML pipelines, including batch and real-time use cases.
• Implement advanced model evaluation systems, including interpretability, hyperparameter optimization, and drift detection.
• Ensure system reliability and performance through rigorous testing and validation., 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.

• Bachelor’s degree in Computer Science, Mathematics, or a related technical field (or equivalent practical experience).
• 15+ years of technical experience in Applied Machine Learning, including producing code and deploying production systems.
• Strong programming skills in Python, Scala, Java, or C++, with expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch).
• Proven experience with Bayesian Neural Networks, Bayesian Learning, and Reinforcement Learning.
• Strong math background in probability, statistics, and optimization.
• Experience with building scalable AI/ML systems using technologies like Spark, Kafka, and distributed architectures.
• Familiarity with advanced ML techniques, including Mixture of Models, Ensemble Techniques, multitask learning, and sharded architectures., * Ph.D. in a relevant technical field with 15+ years of experience in AI/ML research and engineering.
• Expertise in architecting and leading large-scale AI/ML systems with enterprise-level impact.
• Hands-on experience with multitask and multi-objective optimization systems.
• Experience in designing knowledge-driven systems and integrating external knowledge sources.
• Familiarity with foundational models, transformers, and their role in interoperating with Bayesian systems.
• Exceptional leadership, collaboration, and communication skills in complex, matrixed organizations.
• Strong track record of publishing research or developing novel AI/ML techniques.

• Lead groundbreaking applied research in Bayesian systems, distributional reinforcement learning, and multi-modal architectures to drive novel advances in AI and Foundational Intelligence (Ranking, Recommendations, Personalization) to fill out gaps in the Long Tail Curve of Discovery in order to grow the Business Offerings on both Guest and Host Long Tail Ends
• Bridge the gap between theoretical AI/ML advancements and real-world production systems
• Ensure that new research can be effectively applied and scaled to meet practical needs., Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits. Pay Range $296,000-$370,000 USD Go ad-free with Premium ×, Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits. Pay Range $296,000-$370,000 USD

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., 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.