Top AI & Machine Learning Jobs

Design, build, evaluate, and deploy multi-step agentic AI systems, including autonomous and multi-agent workflows. Develop orchestration frameworks, integrate LLMs with tools and APIs, and create evaluation systems measuring reliability, latency, cost, safety, and task success. Collaborate with product, engineering, and research teams to deliver production-grade solutions while mitigating prompt injection, hallucinations, unsafe tool use, and other agentic-system risks.

Lead the design, development, and operation of Expedia’s shared GenAI platform, including scalable backend services, multi-agent infrastructure, APIs, guardrails, intelligent LLM routing, model deployments, fine-tuning pipelines, evaluation standards, and observability. Partner with product and engineering teams to deliver secure, reliable, reusable AI capabilities at global scale.

Uses SQL, Python or R, experimentation, statistical modeling, and large-scale datasets to optimize growth marketing. Designs A/B tests, incrementality and causal analyses; builds analytical datasets, dashboards, models, and automated reporting; and translates complex findings into actionable recommendations for Marketing, Product, Engineering, and Analytics partners.

Leads Expedia B2B’s analytics organization, managing analytics managers and senior analysts while shaping multi-year analytics roadmaps and business strategy. Partners with senior stakeholders to drive measurable outcomes, guides statistical and machine learning applications, establishes analytics engineering and visualization practices, develops talent, and collaborates with data engineering, product, and machine learning teams on scalable solutions.

Develop and productionize scalable machine learning systems for content understanding, search, ranking, recommendations, moderation, computer vision, and generative AI. Responsibilities include data preparation, feature engineering, model training, evaluation, deployment, monitoring, optimization, batch and real-time pipelines, RAG and LLM applications, system design, observability, and cross-functional technical leadership.

Design, implement, and deploy end-to-end ML models and data pipelines for ranking, recommendations, search, and personalization. Build scalable data quality checks, monitoring, and evaluation (A/B tests, offline/online) to ensure reliable, fair, production-grade AI. Collaborate with product, analytics, and engineering to translate business needs into ML solutions and drive iterative improvements.

Design, build, and evaluate multi-step agentic AI systems using autonomous agents, LLMs, tools, APIs, databases, and orchestration frameworks. Develop production-grade agent architectures, evaluation frameworks, and safety measures addressing prompt injection, hallucinations, reliability, latency, cost, and unsafe tool use. Collaborate with product, engineering, and research teams while applying current research to improve personalized travel experiences.

Analyze large, multi-source datasets using SQL and Python, R, or similar tools to generate insights for hotel partnerships and commercial decisions. Build reusable datasets, reporting solutions, visualizations, dashboards, and basic data workflows. Apply descriptive statistics, regression, experimentation, and causal analysis while clearly communicating assumptions and recommendations to technical and non-technical stakeholders. Collaborate with analytics, product, engineering, data, and commercial teams.

Provides technical leadership for end-to-end machine learning solutions supporting metasearch bidding and auction strategies. Owns model development, experimentation, production deployment, monitoring, and post-launch optimization. Defines technical roadmaps and system designs, leads multi-quarter initiatives, partners with engineering and business stakeholders, mentors scientists, and applies statistical experimentation, causal inference, optimization, and machine learning to deliver measurable business outcomes.

Designs, builds, evaluates, and deploys multi-step agentic AI systems, including autonomous and multi-agent architectures. Integrates LLMs with tools, APIs, databases, and code execution environments. Owns evaluation frameworks measuring reliability, safety, latency, cost, and task success. Collaborates with product, engineering, and research teams to deliver production-grade solutions while mitigating risks such as prompt injection, hallucination, and unsafe tool use.