Top AI & Machine Learning Jobs

Lead planning and delivery of applied ML recommendations and relevance initiatives for B2B products. Translate partner needs into technical requirements, define metrics, manage experiments, coordinate cross-functional teams, and drive model development, testing, deployment, and post-launch learning to improve recommendation quality and partner outcomes.

Mid-level product analytics role partnering with product and engineering to analyze large datasets, design experiments, build statistical and ML models, measure success metrics, optimize user experience with GenAI-driven personalization, and deliver actionable insights and scalable analytics solutions to improve product outcomes.

Lead strategy and implementation of Expedia Group's AI evaluation framework. Build metrics, tooling, and governance to measure accuracy, safety, transparency, and performance across AI experiences. Partner with engineering, data science, and product teams to design instrumentation, publish scorecards and recommendations, and drive alignment on evaluation standards and responsible deployment.

Lead the applied ML science team for Marketing to build production ML models and optimization systems for bidding and capital allocation. Own roadmap and OKRs, drive experimentation and measurement, ensure MLOps and production reliability, hire and develop talent, and partner cross-functionally to deliver measurable business impact.

Operate and optimize AI-driven production systems in a cloud environment using DevOps and SRE principles, focusing on reliability and automation.

Lead end-to-end design, build, and deployment of production-scale GenAI, RAG, and multi-agent systems for growth marketing. Responsibilities include training and adapting LLMs (LoRA/QLoRA, RLHF/DPO), building multimodal pipelines, vector memory and embedding systems, agent orchestration, observability/safety, and mentoring teams while partnering with product and design.

Use data, experimentation, and modeling to grow brands. Design and evaluate marketing experiments (A/B, causal inference), build models and datasets with SQL and Python/R, create dashboards and presentations, automate reporting, and enable self‑serve analytics for marketing and product stakeholders.

Design, build, and maintain scalable ML infrastructure and data pipelines for training, deployment, and serving. Optimize compute and serving performance, implement MLOps practices (CI/CD, monitoring), collaborate with ML scientists, and mentor junior engineers to productionize ranking, recommendation, and pricing models.

Architect, build, and operate end-to-end ML infrastructure for training, deployment, and serving (batch and real-time). Define reliability, observability, and performance standards; design scalable data pipelines; establish MLOps (CI/CD, monitoring, A/B testing); productionize models with ML scientists; own system performance; mentor engineers; evaluate and integrate emerging technologies.