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 |
|---|---|---|
| Senior Manager, ML Ops & Observability Engineer Senior Manager role focused on building and operating MLOps platforms and ensuring end-to-end observability for ML systems within a healthcare company. The role involves leading the design and implementation of platforms for model deployment, monitoring, and lifecycle management, integrating with cloud-native environments and DevOps practices. Key responsibilities include establishing observability tooling, defining reliability signals, implementing testing and validation, and enabling data scientists to move models to production safely. The role also emphasizes people leadership and continuous improvement using SRE principles. | ServeEval Gate | 7 |