> Markdown version of [/jobs/ext/195459-principle-engineer-in-bayesian-large-foundational-systems-and-distributional-reinforcement-learning](https://www.wearedevelopers.com/jobs/ext/195459-principle-engineer-in-bayesian-large-foundational-systems-and-distributional-reinforcement-learning). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Principle Engineer -In Bayesian, Large Foundational Systems, and Distributional Reinforcement Learning

- **Company:** Airbnb
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $296,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), 3d Models, Artificial Intelligence, Artificial Neural Networks, C++ (Programming Language), Code Review, Computer Programming, Distributed Systems, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Tensorflow, Scala (Programming Language), Unstructured Data, Reinforcement Learning, Pytorch, Large Language Models, Multi-Agent Systems, Apache Spark, Model Validation, Reliability of Systems, Generative AI, AI Platforms, Information Technology, Apache Kafka, Machine Learning Operations, GPT
- **Published:** May 22, 2026
- **Apply:** https://www.workingnomads.com/jobs/principle-engineer-in-bayesian-large-foundational-systems-and-distributional-reinforcement-learning-airbnb

## About the Role

* Bachelor'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.

## Description

We are seeking a seasoned Principal AI/ML Researcher and Engineer with deep expertise in Bayesian Learning, and Distributional Reinforcement Learning (RL) to lead the advanced research and development of cutting-edge intelligence AI models. These systems will integrate foundational Bayesian frameworks with advanced architectures, including Mixture of Models, multi-pass sharded systems, multitask and multi-objective optimization, and external knowledge incorporation. Additionally, the role involves innovating ways to interoperate and integrate Large Language Models (LLMs) and Large Multimodal Models (LMMs) with Reasoning, Planning, and Decisioning abilities into the Bayesian frameworks to create a seamless foundational model fabric that synergizes with diverse model ecosystems.The role will require ensuring these models and supporting systems perform efficiently at scale, integrating them into live systems that directly impact product and user experience.

Our goal is to build next-generation AI platforms that redefine personalization, decision-making, and intelligence across diverse applications. You will work on developing production-level systems, collaborate with cross-functional teams, and play a pivotal role in shaping our AI/ML strategy.

Relevance and Impact of This Role

This role has the potential to fundamentally transform Airbnb's AI stack from primarily deterministic prediction systems into probabilistic, adaptive, uncertainty-aware intelligence systems capable of reasoning under ambiguity and continuously learning from dynamic environments. In the short term, the impact comes from improving personalization quality, ranking robustness, uncertainty estimation, exploration strategies, and adaptive decision-making across guest and host experiences. Bayesian and reinforcement learning systems would enable Airbnb to move beyond static optimization toward probabilistic and policy-driven intelligence capable of handling sparse data, cold-start problems, long-tail discovery, evolving preferences, and uncertain marketplace dynamics. Guests would receive more adaptive and exploratory recommendations, while hosts and internal systems would benefit from improved forecasting, dynamic optimization, risk-aware decisioning, and more resilient personalization systems.

In the medium term, Airbnb could evolve into a deeply adaptive learning ecosystem where foundational models, reinforcement learning systems, probabilistic reasoning frameworks, and multi-agent intelligence continuously coordinate to optimize long-term marketplace outcomes. Instead of isolated models making independent predictions, the platform would increasingly operate through Bayesian intelligence fabrics, reinforcement-driven optimization systems, and uncertainty-aware decisioning architectures that dynamically learn from behavioral feedback, marketplace conditions, and evolving user intent. This would significantly strengthen long-tail discovery, adaptive exploration, cross-domain personalization, and marketplace resilience while enabling more intelligent balancing between user satisfaction, ecosystem health, supply-demand dynamics, and business growth objectives.

In the long term, this role helps establish Airbnb's strategic leadership in adaptive probabilistic intelligence and continuously learning AI ecosystems. The systems developed under this role become the foundational intelligence substrate connecting Bayesian learning, reinforcement learning, foundational models, reasoning systems, multi-agent orchestration, and large-scale personalization into a unified adaptive architecture. Airbnb would evolve beyond a platform that merely predicts preferences into an intelligent probabilistic ecosystem capable of reasoning under uncertainty, adapting policies dynamically, learning from sparse and evolving signals, and coordinating long-horizon optimization across the entire marketplace. Over time, this could position Airbnb as one of the most advanced real-world adaptive intelligence platforms in the consumer internet - where AI systems continuously balance exploration, exploitation, uncertainty, personalization, and ecosystem optimization in ways
that become increasingly difficult for competitors to replicate., * 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.

Model Development:

 * 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.

Technical Leadership:

 * 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.

Operational Excellence:

 * 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., We are seeking a seasoned Principal AI/ML Researcher and Engineer with deep expertise in Bayesian Learning, and Distributional Reinforcement Learning (RL) to lead the advanced research and development of cutting-edge intelligence AI models. These systems will integrate foundational Bayesian frameworks with advanced architectures, including Mixture of Models, multi-pass sharded systems, multitask and multi-objective optimization, and external knowledge incorporation. Additionally, the role involves innovating ways to interoperate and integrate Large Language Models (LLMs) and Large Multimodal Models (LMMs) with Reasoning, Planning, and Decisioning abilities into the Bayesian frameworks to create a seamless foundational model fabric that synergizes with diverse model ecosystems.The role will require ensuring these models and supporting systems perform efficiently at scale, integrating them into live systems that directly impact product and user experience.

Our goal is to build next-generation AI platforms that redefine personalization, decision-making, and intelligence across diverse applications. You will work on developing production-level systems, collaborate with cross-functional teams, and play a pivotal role in shaping our AI/ML strategy.

Relevance and Impact of This Role

This role has the potential to fundamentally transform Airbnb's AI stack from primarily deterministic prediction systems into probabilistic, adaptive, uncertainty-aware intelligence systems capable of reasoning under ambiguity and continuously learning from dynamic environments. In the short term, the impact comes from improving personalization quality, ranking robustness, uncertainty estimation, exploration strategies, and adaptive decision-making across guest and host experiences. Bayesian and reinforcement learning systems would enable Airbnb to move beyond static optimization toward probabilistic and policy-driven intelligence capable of handling sparse data, cold-start problems, long-tail discovery, evolving preferences, and uncertain marketplace dynamics. Guests would receive more adaptive and exploratory recommendations, while hosts and internal systems would benefit from improved forecasting, dynamic optimization, risk-aware decisioning, and more resilient personalization
systems.

In the medium term, Airbnb could evolve into a deeply adaptive learning ecosystem where foundational models, reinforcement learning systems, probabilistic reasoning frameworks, and multi-agent intelligence continuously coordinate to optimize long-term marketplace outcomes. Instead of isolated models making independent predictions, the platform would increasingly operate through Bayesian intelligence fabrics, reinforcement-driven optimization systems, and uncertainty-aware decisioning architectures that dynamically learn from behavioral feedback, marketplace conditions, and evolving user intent. This would significantly strengthen long-tail discovery, adaptive exploration, cross-domain personalization, and marketplace resilience while enabling more intelligent balancing between user satisfaction, ecosystem health, supply-demand dynamics, and business growth objectives.

In the long term, this role helps establish Airbnb's strategic leadership in adaptive probabilistic intelligence and continuously learning AI ecosystems. The systems developed under this role become the foundational intelligence substrate connecting Bayesian learning, reinforcement learning, foundational models, reasoning systems, multi-agent orchestration, and large-scale personalization into a unified adaptive architecture. Airbnb would evolve beyond a platform that merely predicts preferences into an intelligent probabilistic ecosystem capable of reasoning under uncertainty, adapting policies dynamically, learning from sparse and evolving signals, and coordinating long-horizon optimization across the entire marketplace. Over time, this could position Airbnb as one of the most advanced real-world adaptive intelligence platforms in the consumer internet - where AI systems continuously balance exploration, exploitation, uncertainty, personalization, and ecosystem optimization
in ways that become increasingly difficult for competitors to replicate.


## Related Videos

- [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application)
- [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [TresJS a new declarative ThreeJS as Vue components](https://www.wearedevelopers.com/videos/543-tresjs-a-new-declarative-threejs-as-vue-components)
- [Debugging Machine Learning Code](https://www.wearedevelopers.com/videos/71-debugging-machine-learning-code)
- [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)