Principal Engineer – Bayesian, Large Foundational Systems, Distributional Reinforcement Learning

This is a fully remote position, open to applicants in United States.

📋 Description

• Spearhead innovative applied research in Bayesian systems, distributional reinforcement learning, and multi-modal architectures to facilitate groundbreaking advancements in AI and Foundational Intelligence (Ranking, Recommendations, Personalization), addressing the gaps in the Long Tail Curve of Discovery to enhance Business Offerings for both Guest and Host Long Tail Ends.

• Connect theoretical AI/ML breakthroughs with practical production systems.

• Ensure the effective application and scalability of new research to fulfill real-world demands.

• Define and lead the architecture of large-scale AI systems based on Bayesian Frameworks at Airbnb.

• Create multi-pass sharded Bayesian along with Discriminative/Generative single to multi-agent systems aimed at achieving scale and efficiency.

• Integrate Mixture of Models and Agents, multitask learning, multi-objective optimization, and external knowledge systems into model frameworks.

• Innovate strategies to interact with LLMs, LRMs, LMMs, and transformer-based architectures, ensuring smooth integration and collaboration within the AI ecosystem through AI Multi-Agentic Frameworks.

• Construct and enhance Bayesian or Markovian Graph chains to incorporate uncertainty estimation, adaptive decision-making, and probabilistic reasoning.

• Develop foundational models by combining Bayesian approaches with Classical ML, L[L/M/R]Ms, and other advanced architectures, ensuring compatibility and synergy.

• Direct technical strategy and direction for AI/ML systems.

• Influence cross-functional teams, including engineering leaders, product managers, and data scientists, to embrace unified intelligence platform methodologies.

⛳️ Requirements

• A Master's degree in Computer Science, Mathematics, or a relevant technical discipline (or equivalent hands-on experience).

• Over 15 years of technical expertise in Applied Machine Learning, encompassing code production and deployment of production systems.

• Proficient programming skills in Python, Scala, Java, or C++, with a strong command of AI/ML frameworks (e.g., TensorFlow, PyTorch).

• Demonstrated experience with Bayesian Neural Networks, Bayesian Learning, and Reinforcement Learning.

• Solid mathematical foundation in probability, statistics, and optimization.

• Experience in constructing scalable AI/ML systems utilizing technologies such as Spark, Kafka, and distributed architectures.

• Knowledge of advanced ML methodologies, including Mixture of Models, Ensemble Techniques, multitask learning, and sharded architectures.

🏝️ Benefits

• This position may also qualify for bonuses, equity, benefits, and Employee Travel Credits.