Principal AI/ML Architect, Applied Field Engineering

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 /Staff AI Engineer - Cortex Code Agentic System

Snowflake

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. About the Role The Cortex Code team is building the future of coding agents for working with data. See our flagship product in action: Cortex Code in Action: Live Demos + AMA. As a Staff/Principal AI Engineer on Cortex Code , you will help define architect agent behavior at enterprise scale by building the agentic systems and methodology that make our users build cutting edge agentic systems that are efficient, repeatable, auditable, and shippable. You’ll partner with modeling, platform, and product leadership to turn customer pain into golden scenarios, metrics, and experiment loops that the whole team can trust. Responsibilities: * Agent strategy & systems: Own major pillars of the quality stack: tuning agent behavior to engage on next generation agentic coding tasks. * Hill-climb infrastructure: Design and evolve pipelines and tooling that support large-scale experimentation, error mining, and iteration on prompts/tools/workflows with clear before/after signals. * Deep analysis & prioritization: Lead postmortems on quality regressions; cluster failure modes; translate findings into a prioritized roadmap for engineering and modeling partners. * Cross-functional leadership: Align product, infra, and applied AI on what “good” means for critical customer workflows; mentor engineers and uplevel eval craft across the team. * Production-minded rigor: Ensure quality systems are dependable in practice—reproducible runs, stable datasets, versioning, and operational clarity when things drift. Requirements: * Bachelor’s degree in Computer Science, Engineering, Statistics, or a related field. Master’s or higher preferred but not a requirement. * 10+ years of experience shipping AI/ML-backed software in production, including Staff-level ownership of technical direction, cross-team delivery, and mentoring. * Strong track record building and operating eval harnesses, measurement, and/or experimentation loops for LLM/agent systems—not only one-off benchmarks. * Proficiency in programming languages such as Python, TypeScript, Go (strong in at least two). * Exceptional communication skills: crisp write-ups, constructive debate, and ability to influence without authority across engineering and product. * (Optional) Experience with data engineering pipelines (dbt, Airflow), data modeling, data analysis, retrieval systems, and semantic layers is a plus. Nice to have * Deep experience with agentic coding tools (IDE agents, CLI agents) and intuition for model strengths, failure modes, and prompting limits. * Background in data engineering (dbt, Airflow), analytics, retrieval / RAG, or semantic layers—highly relevant for data-centric coding agents. * Prior work on LLM observability, safety/guardrails, or quality systems used as release gates in production. You may be a particularly good fit if you * Have built and owned complex quality + data pipelines—substantial state, branching logic, and operational requirements. * Thrive in high-intensity environments with short feedback loops and high standards for rigor. * Take ambiguous “quality is slipping” problems to completion: you care about clear metrics, reproducibility, and sustained improvement—not one-off score bumps. * Are a power user of modern coding agents and care about turning intuition into systematic measurement and team-wide practice. About Snowflake Snowflake is the AI Data Cloud trusted by the world's most innovative companies. We're shipping production-ready AI applications at scale and want you to join us in building the future of how businesses interact with their data through Cortex Code, Cortex agents, Cortex analyst, Cortex search. Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential. Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

Sr Principal Data Scientist

Smartsheet

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Smartsheet is looking for a Sr. Principal Data Scientist to set the technical direction for the ML models and AI sub-agents that drive growth, monetization, efficiency, and retention across the customer lifecycle. You’ll architect the systems, define the modeling and evaluation standards, and shape the roadmap for how Smartsheet uses applied ML and agentic AI to serve millions of customers. The data is unusually rich: petabyte-scale execution data spanning two decades of how real work gets done. You are a recognized technical leader who moves fluidly between architecture, prototype, and production; raises the bar for the data scientists around you; and partners with senior product and engineering leaders to make decisions that compound across the org. You will work primarily with Product and Engineering and will be a part of Smartsheet’s Business Intelligence team. This full-time position reports to the Director of Data Science and is based in Smartsheet’s Bengaluru, India office. You Will: * Set the technical direction for how Smartsheet uses applied ML and agentic AI across the customer lifecycle — what gets built, what doesn’t, and what good looks like * Architect the systems behind the sub-agents: how they ground themselves in evidence, how risk and confidence are calibrated, how decisions are evaluated, and how all of it stays safe and trustworthy at scale * Define the modeling, evaluation, and experimentation standards the team follows — from offline metrics to online rollout to production monitoring * Personally ship the highest-leverage models and sub-agents — the ones that need a senior IC to frame the problem, push through ambiguity, and de-risk the approach * Drive technical decisions on the data foundations and knowledge layer sub-agents reason over, with strong stewardship of privacy, aggregation, and customer trust * Partner with senior Product, Engineering, and Applied AI leaders to shape strategy, roadmap, and investment * Raise the bar for the data science team — mentor staff and principal data scientists, review designs, set hiring standards, and grow the org’s modeling rigor   You Have: * Bachelor’s degree and 12+ years of experience (or 14+ years of experience); advanced degree in a quantitative field (Statistics, CS, ML, Economics, Operations Research, or similar) strongly preferred * Track record of setting technical direction for applied ML and AI work that ships and matters — major initiatives, complex systems, or new modeling paradigms taken from idea to production impact * Deep applied ML expertise across both traditional ML and deep learning: gradient boosting, regularized linear models, transformer-based sequence models, foundation model embeddings, causal ML, contextual bandits, and offline RL * Architect-level grasp of agentic AI systems: tool use, retrieval, multi-step reasoning, evaluation, guardrails, and the patterns for keeping all of it reliable in production * Strong grasp of causal inference for intervention design and lifecycle modeling: uplift modeling, difference-in-differences, propensity scoring, and synthetic control * Solid foundation in statistics and experimental design at scale: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods * Experience operating ML in production at scale — feature engineering and pipelines, model monitoring, drift detection, retraining cadence, and the trade-offs between batch and real-time serving * Proficient in SQL and Python; comfort with ML/LLM tooling at scale (Spark, Databricks, Snowflake, or equivalents), ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM), and visualization tools (Tableau or similar) * Experience leading lifecycle modeling work — churn, expansion, adoption, plan health, lead/account scoring — and business fluency in the SaaS metrics that drive it (NRR, GRR, ARR, and cohort economics) * A pragmatic production bar: latency, cost, monitoring, drift, hallucination, and what happens when the model or sub-agent is wrong * Demonstrated ability to influence senior product and engineering leaders and to mentor staff and principal data scientists — your track record shows people and decisions, not just models, getting better * Comfort operating in deep ambiguity — defining the problem, choosing the approach, and aligning the team when no playbook exists     Get to Know Us: At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together. Equal Opportunity Employer: Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information.  If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.   #LI-Remote