Top AI & Machine Learning Jobs

As an AI Data Engineer, you will develop full stack applications integrating AI capabilities to support pharmaceutical research, enhancing workflows and productivity. Responsibilities include collaborating with researchers, developing scalable backend services, and deploying systems in AWS.

Lead the deployment of data science solutions and capability programs across international markets, translating business needs into actionable insights and guiding stakeholders.

The Senior Director of Applied Intelligence leads a team to tackle AI/ML challenges through fast experimentation, technical leadership, and effective communication, ensuring prototypes are scalable and impactful for commercial uses.

This role involves developing and maintaining AI/ML models for supply chain optimization, predictive analytics, and automation, driving data-driven solutions and enhancing supply chain efficiency.

This role involves developing AI-enabled pipelines for antibody drug conjugate design, conducting experiments, and collaborating with internal and external partners to optimize discovery processes.

As the Manager of AI and Data Science Full Stack Engineer, you will architect, implement and improve AI solutions and data workflows, collaborating with teams to integrate insights into production while ensuring best practices in software development.

The Senior Computational Chemist will lead cheminformatics efforts, design data pipelines, apply machine learning models to drug discovery, and collaborate across teams.

The role involves leading MLOps platform design, ensuring model observability and reliability, and supporting model deployment within cloud environments while coaching engineers on best practices.

The Medical Affairs AI Lead oversees AI transformation for Medical Affairs, prioritizes initiatives, builds prototypes, and collaborates across departments for sustainable solutions.

Leads AI strategy and technical direction for applied AI in Inflammation & Immunology, overseeing portfolio development, partnerships, and governance standards to improve scientific decision-making.