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

🕒 il y a 6 jours

🇺🇸 États-Unis – Télétravail

💵 $296 000 - $370 000 / an

⏰ Temps Plein

🔴 Expert

🧑‍💻 Développeur Full-Stack

🦅 Parrain de Visa H1B

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Airbnb

Site WebLinkedInTous les Emplois

5001 - 10000 employés

Fondée en 2007

👥 B2C

🛍️ eCommerce

💰 Post-IPO Equity en 2020-12

B2C • eCommerce • Hospitality

Airbnb est une place de marché en ligne mondiale qui met en relation des personnes cherchant un hébergement avec des hôtes proposant des options de logement uniques et variées, souvent dans des propriétés résidentielles. Les utilisateurs peuvent réserver des espaces allant d'appartements d'une chambre entière à des maisons complètes et des hôtels de charme. Airbnb propose également des expériences, permettant aux invités de réserver des activités organisées par des locaux, offrant ainsi une expérience de voyage authentique.

Description

• 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. • 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.

🎯 Exigences

• 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.

🏖️ Avantages

• This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.