Pfizer

Seeking a Postdoctoral Fellow to apply foundation models and machine learning to integrate multi-omics data for predictive toxicology in drug safety research. The role involves benchmarking models, developing AI pipelines, and identifying novel biomarkers to predict human toxicity from preclinical data, with a focus on generating peer-reviewed publications and potentially open-source tools.

Senior Manager role focused on designing, developing, and deploying AI agents for enterprise workflow automation, decision enhancement, and intelligent user interactions. Requires experience with agentic orchestration frameworks, vector databases, RAG, and LLM principles, along with leadership in managing technical teams and delivering enterprise-scale projects.

Seeking a Postdoctoral Fellow to develop AI/ML enabled bioprocess models and control strategies for recombinant protein production. The role involves creating hybrid mechanistic-data-driven models, integrating omics data, applying ML for prediction, and developing model predictive control frameworks. It also includes exploring agentic AI for orchestrating model fitting and transfer learning, and designing experiments for data generation and validation.

This role focuses on applying LLMs and agentic AI to scientific workflows in healthcare (Pfizer). The scientist will identify use cases, build reusable workflows and agentic systems, and partner with scientists to ensure adoption and impact. The role emphasizes practical application, scientific credibility, and scalable solutions.

Head of AI Delivery & Enablement (AIDE) at Pfizer, leading strategy and technical direction for applied AI in Inflammation & Immunology. Focuses on translating advances in LLMs, agentic systems, and multimodal AI into reusable capabilities, establishing governance and evaluation standards, and driving AI adoption. Requires strong technical credibility in AI/ML and computational biology, with experience in strategy, portfolio management, and scaling reusable solutions.

Senior Manager, Data & AI Engineer role focused on building and scaling data and AI engineering capabilities for AIT programs. This player-coach role involves end-to-end platform capability design (L4+), coordinating with various teams, and ensuring trusted data consumption and AI adoption. Responsibilities include designing and delivering production-grade data pipelines, semantic layers, model/LLM serving services, and operational tooling with strong engineering discipline. The role also emphasizes cross-functional alignment, AI consumption enablement (semantic/context layers, orchestration patterns), data quality, observability, MLOps/LLMOps, and backend services development.

The GCA China Analytics Innovation Lead at Pfizer will drive analytics innovation, focusing on AI-driven solutions to solve complex business problems in the healthcare commercial organization. This role involves developing and executing an innovation roadmap, championing AI tool adoption, and enabling analytical capabilities within the team. The lead will partner with various stakeholders to identify opportunities, incubate, develop, and implement AI/ML initiatives, and quantify their business impact.

This role focuses on building and deploying full-stack applications that integrate LLM and AI capabilities into pharmaceutical research workflows. Responsibilities include developing backend services for data processing, embedding generation, vector search, and LLM orchestration, implementing RAG systems and agentic architectures, and creating frontend interfaces. The role also involves contributing to semantic frameworks and deploying systems on AWS.

Director of AI Engineering role focused on applying AI automation and advanced analytics in regulated healthcare R&D operations. The role involves leading the definition and delivery of AI solutions for lab and office workflows, strengthening operational decision-making through predictive models, enabling regulated AI systems with governance, optimizing development planning, and engineering/scaling production-grade ML platforms. Requires expertise in predictive modeling, optimization, automation, generative AI, and ML system design, with strong programming skills in Python and ML frameworks, and experience scaling models in cloud/HPC environments. Collaboration across scientific, operational, quality, and business teams is critical.