Principal AI/ML Researcher / Engineer (Bayesian, Large Foundational Systems & Distributional Reinforcement Learning)

Role Overview

Join Airbnb as a Principal AI/ML Researcher / Engineer to lead advanced research and development of intelligence AI models. You will integrate foundational Bayesian frameworks with modern architectures, enabling efficient, large-scale systems that improve personalization and decision-making across diverse applications.

Key Missions

• Lead research and development of cutting-edge AI/ML intelligence models.
• Integrate Large Language Models (LLMs) and Large Multimodal Models (LMMs) with Reasoning, Planning, and Decisioning capabilities inside Bayesian frameworks.
• Define and drive the architecture of large-scale Bayesian-framework-based AI systems, ensuring new research can be applied and scaled in production.

Responsibilities

• Architect and scale AI/ML systems and the Bayesian-based infrastructure that supports experimentation-to-production.
• Innovate interoperability between foundational models (transformers) and Bayesian methods.
• Ensure models perform efficiently at scale and deliver practical enterprise impact.

Requirements

• 15+ years of technical experience in applied machine learning, including producing code and deploying production systems.
• Strong math foundation in probability, statistics, and optimization.
• Proven experience with Bayesian Neural Networks, Bayesian Learning, and Reinforcement Learning.
• Strong programming skills in Python, Scala, Java, or C++, and proficiency with major AI/ML frameworks such as TensorFlow or PyTorch.
• Master’s degree in CS/Math or equivalent; Ph.D. preferred/required with 15+ years of AI/ML research and engineering experience.
• Experience building scalable AI/ML systems using Spark, Kafka, and distributed architectures.
• Familiarity with advanced ML techniques: mixture of models, ensemble techniques, multitask learning, and sharded architectures.
• Hands-on experience with multitask and multi-objective optimization.
• Knowledge-driven systems and integrating external knowledge sources.
• Strong leadership and communication skills in complex, matrixed organizations.
• Track record of publishing research and/or developing novel AI/ML techniques.

Nice-to-Haves (Implied by the posting)

• Experience integrating foundation models and transformers with Bayesian systems.
• Expertise in reasoning/planning/decisioning systems for real applications.