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

Airbnb

1d•$296,000 - $370,000•Remote

About The Position

Airbnb is 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. This role drives Airbnb's evolution toward probabilistic, uncertainty-aware intelligence systems capable of reasoning under ambiguity and learning continuously from dynamic environments. The near-term impact spans improved personalization quality, ranking quality, uncertainty estimation, and adaptive decision-making across guest and host experiences — enabling policy-driven intelligence that handles long-tail discovery, evolving preferences, and complex marketplace dynamics. Longer term, this role helps establish Airbnb's leadership in adaptive probabilistic intelligence by building the foundational substrate that connects Bayesian learning, reinforcement learning, foundational models, multi-agent orchestration, and large-scale personalization into a unified adaptive architecture — where AI systems continuously balance exploration, exploitation, uncertainty, and ecosystem optimization at scale.

Requirements

Master'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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.