Top Hybrid Machine Learning Engineer Jobs

This role involves developing AI/ML architectures, resolving complex issues, and partnering with stakeholders to enhance business outcomes through technology and data.

Lead development, deployment, and monitoring of underwriting, risk, fraud, and payments ML models. Own full modeling lifecycle, feature engineering, validation, A/B testing, cross-functional collaboration, and mentor junior data scientists.

As a Senior Machine Learning Engineer, you'll develop and optimize models for Chewy's Sponsored Ads to enhance product selection and advertising effectiveness. You will collaborate with cross-functional teams, lead model deployment, and publish research to elevate machine learning applications while mentoring junior scientists.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Lead AI/ML recommendation product lifecycle for Growth & Marketing: define strategy and roadmap, gather requirements, collaborate with engineering and data science, run experimentation and KPI measurement, manage risks, and drive product launches and optimization to meet business objectives.

Lead design, development, and deployment of predictive ML models and anomaly detection. Perform extensive EDA, feature engineering, model evaluation, A/B testing, and customer segmentation. Build monitoring and governance for models, collaborate with Product/Engineering/Marketing, and ensure scalable data pipelines and model integration to drive member acquisition, retention, and product optimization.

Design, build, and deploy end-to-end AI/ML solutions using LLMs, agentic and probabilistic methods. Scope problems, prototype, evaluate and benchmark models, integrate production APIs and cloud infrastructure, apply responsible AI practices, and communicate results to stakeholders while mentoring colleagues.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

As a Principal AI/ML Engineer, you will lead the development of machine learning solutions for advertising, focusing on optimization, personalization, and deploying models within a highly scalable infrastructure.

Own evaluation, measurement, and optimization of production LLM-powered features. Design reproducible evaluation frameworks, run experiments and analyses to identify failure modes and regressions, build metrics and dashboards, partner with engineering to productionize improvements, and mentor teammates on experimental design and measurement best practices.