Pfizer has 19 active AI-related job listings. The majority of these roles are focused on agents, accounting for 32% of the openings, followed by data roles at 26%. Engineering is the most frequent function, with 14 positions. The company is actively hiring for roles involving agent orchestration, model serving, and RAG. Over the last 30 days, Pfizer posted 45 new AI roles.
Currently tracking 10 active AI roles, down 57% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$358k (avg $176k).
Pfizer currently has 18 active AI-related roles in our index. The most common open titles are: Clinical Development Scientist, Director (Inflammation and Immunology) (2), AI Data Engineer--LLMs / Agentic Systems, AI/ML Engineer - Vaccine Research, Business Title Senior Scientist, Sterile Injectables Design, Director of AI Engineering – Vaccine R&D Operations Enablement. Most positions are in Engineering and Product.
Pfizer's active AI hiring is concentrated in: application (44%), agents (28%), post-training (11%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Pfizer is hiring AI talent in: United States (13 roles), China (2 roles), Spain (1 role), India (1 role).
Job postings at Pfizer most frequently reference: llm observability, model serving, rag, agent orchestration, fine tuning.
In the past 30 days, Pfizer has posted 24 new AI-related roles. That is a -27% change versus the prior 30 days (33 → 24).
| Title | Stage | AI score |
|---|---|---|
| Business Title Senior Scientist, Sterile Injectables Design The Senior Scientist, SID Predictive Sciences role at Pfizer focuses on leading and deploying end-to-end Predictive Science activities for sterile injectable drug product design. This involves integrating molecular dynamics (MD) simulations with machine learning (ML) and Generative AI (GenAI) to understand molecular interactions, material properties, and formulation behavior. The role requires building predictive models from simulation data, utilizing vector databases for knowledge retrieval, and applying GenAI to extract insights from scientific literature and automate workflows. The scientist will collaborate with global teams to drive the adoption of these digital tools in product development and present outcomes. | Data | 7 |