Principal Engineer -In Bayesian, Large Foundational Systems, and Distributional Reinforcement Learning
Core
Lead advanced research and development of cutting-edge intelligence AI models integrating Bayesian frameworks, Large Language Models (LLMs), and Large Multimodal Models (LMMs) to create a foundational model fabric for personalization, decision-making, and adaptive intelligence.
Role type
Principal AI/ML Researcher and Engineer (Bayesian & Distributional RL)
Builds
Production-level AI/ML systems, foundational model fabric, and large-scale Bayesian frameworks for guest and host experiences.
Domain
Travel marketplace, Probabilistic AI, Reinforcement Learning, Foundational Models
Deliverable
production ML models
Required skills
Bayesian Learning, Distributional Reinforcement Learning, Mixture of Models, Multi-objective optimization, LLM/LMM integration, Scalable system architecture, Probability & Statistics, Python, Scala, Java, C++, TensorFlow, PyTorch, Spark, Kafka
Preferred skills
Ph.D. in technical field, Enterprise-level AI/ML system leadership, Knowledge-driven system design, Research publication track record
Technologies
TensorFlow, PyTorch, Spark, Kafka
Responsibilities
Lead applied research in Bayesian systems and distributional RL; Define architecture of large-scale Bayesian Framework-based AI systems; Build and refine Bayesian/Markovian Graph chains for uncertainty estimation; Lead technical direction and strategy for AI/ML systems; Develop and productionize scalable AI/ML pipelines; Mentor engineers and champion best practices.
Seniority
Principal, hands-on IC with strategy & mentorship
