Principal ML Engineer (Applied/Systems)

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

Airbnb

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. ABOUT THE ROLE 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 drives Airbnb's evolution toward probabilistic, uncertainty-aware intelligence systems capable of reasoning under ambiguity and learning continuously from dynamic environments. The near-term impact spans improved personalization quality, ranking quality, uncertainty estimation, and adaptive decision-making across guest and host experiences — enabling policy-driven intelligence that handles long-tail discovery, evolving preferences, and complex marketplace dynamics. Longer term, this role helps establish Airbnb's leadership in adaptive probabilistic intelligence by building the foundational substrate that connects Bayesian learning, reinforcement learning, foundational models, multi-agent orchestration, and large-scale personalization into a unified adaptive architecture — where AI systems continuously balance exploration, exploitation, uncertainty, and ecosystem optimization at scale. WHAT YOU WILL DO RESEARCH & INNOVATION: * 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. ARCHITECT AND DESIGN: * 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. COLLABORATION: * 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. MINIMUM QUALIFICATIONS * 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. PREFERRED QUALIFICATIONS * 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. Your Location: This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list . If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process.  We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application. How We'll Take Care of You: Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.   Pay Range $296,000—$370,000 USD

Principal AI/ML Researcher / Engineer In Reasoning, Planning, and Decision-making systems

Airbnb

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. ABOUT THE ROLE We are seeking a Principal / Distinguished AI/ML Researcher and/or Engineer with deep experience in reasoning, planning, and decision-making systems. This role is ideal for individuals who have architected post-training intelligence frameworks, integrated Large Reasoning Models (LRMs) with Knowledge Graphs, and applied Reinforcement Learning (RL) as a first-class component of adaptive planning and control. You will be responsible for inventing, scaling, and operationalizing intelligent decisioning substrates that blend symbolic and sub-symbolic methods, enabling next-generation AI systems that go beyond pattern recognition into the realm of deliberation, foresight, and agency. Our mission is to build cognitive AI systems that combine post-trained foundational models, explicit memory and knowledge, and recursive planning strategies to power sophisticated real-world decisioning in personalized environments. You will collaborate across disciplines and influence company-wide AI architecture. A core dimension of this role is the design and deployment of multi-agent systems, where reasoning, planning, and decisioning are distributed across networks of intelligent agents. You will formulate coherent, synergistic strategies that enable agents to cooperate, negotiate, and align objectives, ensuring that distributed intelligence converges to purposeful, high-quality outcomes across contexts. RELEVANCE AND IMPACT OF THIS ROLE  This role advances Airbnb's AI capabilities toward reasoning, planning, and adaptive decision-making across complex real-world environments. The near-term impact spans improved decision quality, contextual intelligence, adaptive personalization, and operational coordination across guest and host workflows — introducing goal-directed reasoning systems capable of handling ambiguity, constraints, trade-offs, and multi-step planning. Guests benefit from more intelligent planning and assistance, while hosts and internal teams gain systems capable of adaptive optimization, dynamic recommendations, policy-aware decisioning, and intelligent workflow orchestration. Longer term, this role helps establish Airbnb's leadership in cognitive AI systems and distributed intelligence architectures. The technologies developed here become the decisioning and reasoning substrate underlying the broader ecosystem — enabling AI systems that can deliberate, coordinate, adapt, and make coherent long-horizon decisions across multiple agents and environments. Over time, this positions Airbnb as an intelligent coordination and planning platform where AI systems actively reason, plan, and coordinate actions across the marketplace in ways that continuously improve user outcomes, ecosystem health, and strategic adaptability. WHAT YOU WILL DO RESEARCH & INNOVATION * Drive foundational and applied research in reasoning engines, planning architectures, and decision-making frameworks at scale in order to incorporate genAI into the ranking / recommendation / personalization stack in both single model to multi-agent ( system ) level intelligence with objective to grow the business (new user growth, abandoned user, long tailed user) in existing and new business areas while supporting Multi-Modal NL → Conversational Interfaces. * Advance techniques in LLM/LRM post-training, reinforcement learning–based decisioning, and knowledge-integrated agents. * Design methods for plan induction, value estimation, and contingency modeling within intelligent agents. * Explore and validate protocols for distributed reasoning and joint planning among cooperative agents in multi-agent systems. SYSTEM DESIGN & ARCHITECTURE * Architect RPD systems that integrate post-trained LLMs/LRMs, graph-structured memory (e.g., KGs), and RL-driven controllers. * Design recursive task planners, search-based or policy-based reasoners, and belief-state trackers that can interoperate with large model substrates. * Ensure modularity and extensibility through multi-agent frameworks, agentic substrates, and declarative planning pipelines. * Define communication protocols, coordination strategies, and cross-agent knowledge alignment mechanisms to foster emergent cooperative intelligence. MODEL DEVELOPMENT * Build and evolve stateful, dynamic models that combine supervised learning with online/offline reinforcement, simulation-based rollouts, and symbol grounding. * Implement hybrid pipelines that couple learned embeddings, prompted generative models, and graph-theoretic inference. * Optimize systems for adaptive exploration, planning horizon control, and policy robustness. * Develop frameworks for distributed value propagation, multi-agent credit assignment, and global planning from local agents. TECHNICAL LEADERSHIP * Set direction for planning/reasoning infrastructure within the AI/ML platform strategy. * Serve as the technical conscience and architectural leader across high-stakes AI initiatives involving autonomous agents or high-fidelity decision pipelines. * Mentor teams in systems thinking, causal modeling, symbolic-connectionist integrations, and long-term planning under uncertainty. * Lead development of multi-agent reasoning systems, defining principles for inter-agent knowledge exchange, goal delegation, and cooperative decision resolution. COLLABORATION * Work across disciplines—product, infra, and design—to translate ambiguous product intent into multi-stage reasoning pipelines. * Partner with researchers, ontologists, and ML engineers to encode world knowledge, goals, and values into usable inference artifacts. * Contribute to a company-wide understanding of what it means to make intelligent choices, not just predictions. * Collaborate with internal teams on distributed agent coordination, shared memory protocols, and policy harmonization across decision surfaces. OPERATIONAL EXCELLENCE * Productionize real-time reasoning loops with low-latency inference, caching, retrieval-augmented generation, and streaming updates to symbolic memory. * Deploy post-training hooks for inserting logic, constraints, and domain priors into existing large models. * Create advanced monitoring, attribution, and evaluation pipelines for agent behavior and decision quality. * Operationalize multi-agent orchestration, ensuring reliable and fault-tolerant communication and decision propagation. MINIMUM QUALIFICATIONS * Masters or equivalent in Computer Science, AI, Cognitive Science, or related fields. * Recent published work or patents in AI, Cognitive Science, or related fields.  * 15+ years in AI/ML, including post-training architectures and production-scale reasoning systems. * Advanced coding proficiency in Java, Python, C++, or similar, with experience in ML/RL frameworks (e.g., PyTorch, Ray, JAX, RLlib) at scale. * Proven experience integrating LLMs/LRMs with Knowledge Graphs or structured world models. * Deep understanding of Reinforcement Learning and its application to decisioning and planning. * Fluency in hybrid model architectures: connectionist-symbolic fusion, retrieval-based agents, or goal-directed transformers. * Experience working on multi-agent coordination, distributed RL, or cooperative inference systems. PREFERRED QUALIFICATIONS * Ph.D. in AI, Machine Learning, Robotics, Cognitive Systems, or related areas. * Published work or patents in multi-agent reasoning, plan synthesis, knowledge-augmented learning, or generative control. * Experience in cognitive architectures, neuro-symbolic systems, or agent-based simulation environments. * Demonstrated ability to lead cross-functional research-to-production transitions. * Experience with memory architectures, task graphs, or semantic program induction. * Prior work on distributed intelligence platforms with explicit agent interaction models and collective decision-making logic. Your Location: This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list . If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process.  We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application. How We'll Take Care of You: Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.   Pay Range $296,000—$370,000 USD

Principal Machine Learning Engineer - Applied AI & LLMs (x/f/m)

Doctolib

What you’ll do We are looking for a Principal Machine Learning Engineer to join the DS & AI organization at Doctolib. This is a transversal force multiplier role operating across all AI teams: Clinical, Productivity, Phone Assistant and ASR. Rather than working within a single feature team, you will own the cross-cutting technical challenges: defining how Doctolib's product and AI teams build agentic solutions, standardizing ML evaluation frameworks, and spreading strong engineering practices across the org. You will unblock teams, shape architecture decisions, and take on the deep technical work that requires senior ML judgment at scale, directly impacting the daily lives of 400,000 healthcare professionals and the health of 80 million patients. Building at Doctolib means delivering innovative products that measurably improve outcomes for care teams and patients alike.   Technical Strategy & Architecture * Own cross-team ML architecture decisions, from model selection to production deployment patterns and strategic build vs. buy decisions * Define and drive adoption of rigorous evaluation frameworks across all AI teams, setting the standard for how Doctolib measures model and agentic performance * Establish shared production infrastructure patterns for AI: model and prompt versioning, guardrails, uncertainty quantification, cost optimization, and observability * Lead the technical design of agentic solutions and define how product teams across Doctolib build on top of AI capabilities * Elevate technical standards across the DS & AI org by influencing Staff and Senior ML Engineers through architecture reviews, technical guidance, and practice propagation * Partner with Product, Legal, and Compliance teams to ensure safe rollout and meet EU regulatory expectations (GDPR, MDR, EU AI Act)   Who you are Before you read on, if you don't have the exact profile described below, but you feel this job description matches your skill set, we still encourage you to apply. You'll be a great fit if: * You have 10+ years in ML/AI with 3+ years at Staff+ or Principal level leading complex, multi-team technical initiatives * You have deep expertise in at least two of: clinical NLP, LLM fine-tuning and evaluation, automatic speech recognition, RAG systems, or reinforcement learning * You are expert in Python, PyTorch/Transformers for training, and vLLM for inference with a track record deploying ML systems in production (AWS/GCP) * You have a PhD in Computer Science, AI, Statistics, or related field (or equivalent research experience) * You have exceptional communication skills and are able to align diverse stakeholders and explain complex technical decisions Now it would be fantastic if you have: * Publications in top-tier ML/AI conferences (NeurIPS, ICLR, ACL) or medical informatics venues * Experience with EU healthcare regulations (GDPR, MDR, AI Act) * Prior work with clinical data or healthcare applications   Life at Doctolib Tech * Our solutions are built on a single fully cloud-native platform supporting web and mobile interfaces across multiple languages and healthcare specialties. * Our stack includes Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native. * We leverage AI ethically to empower patients and professionals. Discover our AI vision here. Want to learn more about our tech culture and environment? Visit the Doctolib Tech site.   What we offer * Free comprehensive health insurance for you and your children * 25 days of paid vacation per year, plus up to 14 days of RTT * Free mental health and coaching services through our partner Moka.care * Work from abroad for up to 10 days per year thanks to our flexibility days policy * Lunch vouchers with Swile card (€8.50 value, €4.50 covered by Doctolib) * Work Council subsidy for sports club memberships or creative classes * 50% reimbursement of your public transport subscription * Parent Care Program: one additional month of leave on top of legal parental leave * For caregivers and workers with disabilities: remote policy adaptation, extra medical leave, and psychological support * Relocation support for international mobility * Access to premium AI tools for development and dedicated training   The interview process * HR Screen * Hiring Manager Interview * Technical Interview * Technical Case Study * Behavioral Interview * At least one reference check We want your experience to be clear, respectful, and transparent. Learn more on our candidate experience page.   Job details * Permanent position * Full-time * Paris, France * Hybrid work mode (up to 2 remote days per week) * Start date: as soon as possible   We welcome everyone At Doctolib, we believe in improving access to healthcare for everyone - regardless of where you come from or what you look like. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination. We respect and celebrate diversity! The more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply regardless of gender, religion, age, sexual orientation, ethnicity, disability, or place of origin. To ensure equal opportunities, we invite you to exclude personal information (e.g., pictures, age) from your applications. If you have a disability, let us know if there's any way we can make the interview process smoother for you! Join us in building the healthcare we all dream of.   Your data privacy All information transmitted via this form is processed by Doctolib for application management. For more on how we process your data, click here. For inquiries or to exercise your rights, contact us at hr.dataprivacy(at)doctolib.com.