Senior AI Research Engineer

Help Build a New AI-Native Operating Model

At Pfizer, we're investing in the future of AI-enabled drug discovery.

This role is about more than using AI to improve existing ways of working. We are building a new operating model in which small, highly capable teams combine human expertise with AI to achieve outcomes and move at a pace that previously required much larger organizations.

We are looking for people who want to help invent and champion that model: individuals willing to rethink how work is organized, how problems are decomposed, what can be delegated to intelligent systems, and how humans and AI work together most effectively. Successful candidates will use AI not simply as a productivity tool, but as a means to fundamentally transform how scientific and technical work gets done—and will help others across R&D adopt these new ways of working.

Why This Role Is Different

Own Meaningful Problems

You'll own critical AI capabilities from architecture through production deployment. Rather than contributing to isolated components, you'll lead the design and delivery of end-to-end systems that directly enable scientific discovery.

Work Where AI Meets Real-World Impact

The systems you build will support researchers tackling some of the toughest challenges in medicine, helping accelerate insights across discovery, development, and translational science.

Operate at the Frontier of AI

You will work across technologies and domains including:

• Large Language Models (LLMs)
• Agentic AI Systems
• Multimodal Learning
• Scientific Machine Learning
• Retrieval-Augmented Generation (RAG)
• Knowledge Graphs
• Distributed Training and Inference
• AI Platforms and MLOps
• AI Evaluation and Benchmarking

Accelerate Your Growth

You'll work alongside experts in AI, engineering, computational biology, and drug discovery while taking on increasing technical ownership and influence.

Build, Ship, Learn, Repeat

We value engineers who can move from ambiguity to execution, convert ideas into working systems, measure outcomes, and continuously improve based on real-world feedback.

The Opportunity

As a Senior AI Research Engineer, you will build the machine learning systems that transform innovative models into secure, reliable, and scalable capabilities for scientists across Pfizer.

You will:

• Own an end-to-end subsystem, platform capability, or workflow
• Make sound architecture and design tradeoff decisions
• Deliver production-quality software and AI solutions
• Drive technical execution for complex projects
• Influence engineering standards and best practices
• Help less-experienced engineers grow through collaboration, code reviews, and technical coaching

Success in this role requires strong engineering fundamentals, demonstrated experience delivering production systems, and the ability to operate effectively in an environment where science, technology, and business impact intersect.

What You'll Do

Build the AI Systems Behind Tomorrow's Scientific Breakthroughs

• Design, develop, and deploy production-grade AI systems supporting multimodal biomedical models and intelligent agents
• Engineer data pipelines, retrieval systems, evaluation frameworks, knowledge systems, inference services, and human-in-the-loop workflows
• Create scalable software that enables AI capabilities to move beyond experimentation and into everyday scientific use

Own End-to-End Technical Delivery

• Translate scientific and business needs into technical solutions
• Lead architecture, implementation, testing, deployment, monitoring, and continuous improvement for your area of ownership
• Drive projects from concept through production and operational support

Turn Research Into Impact

• Partner closely with AI scientists, computational biologists, data engineers, and platform teams
• Transform promising prototypes into reusable, reliable, and maintainable systems
• Develop solutions with clear interfaces, measurable performance, and long-term sustainability

Make Thoughtful Engineering Tradeoffs

• Balance performance, scalability, cost, reliability, security, privacy, maintainability, and speed of delivery
• Apply sound engineering judgment when evaluating technical approaches and system architecture decisions

Build Trusted and Responsible AI

• Strengthen model evaluation, observability, reproducibility, lineage, security, and operational resilience
• Implement practices that support responsible and trustworthy AI deployment within scientific environments

Raise the Engineering Bar

• Contribute to architecture reviews, code reviews, engineering standards, and reusable technical patterns
• Troubleshoot complex problems and improve engineering quality across the organization
• Provide guidance and support to less-experienced engineers through collaboration and mentorship

Deliver Measurable Outcomes

• Define success metrics and operational indicators
• Continuously improve performance, reliability, usability, and scientific impact
• Help teams adopt AI with confidence and scale

What Makes You a Strong Fit

Education & Experience

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