[Remote] Principal AI/ML Researcher / Engineer In Bayesian, Large Foundational Systems, and Distributional Reinforcement Learning

Note: The job is a remote job and is open to candidates in USA. Airbnb is a global hospitality company that connects hosts and guests through unique stays and experiences. They are seeking a seasoned Principal AI/ML Researcher and Engineer with expertise in Bayesian Learning and Distributional Reinforcement Learning to lead the development of advanced AI models that enhance personalization and decision-making. The role involves collaborating with cross-functional teams to innovate and integrate AI systems that operate efficiently at scale.

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

Skills

• 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
• 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

Benefits

• Bonus
• Equity
• Benefits
• Employee Travel Credits

Company Overview

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