Senior Principal AI Research Engineer
$139.1k - $231.9kPfizer Inc
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
- 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
- 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
- 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
- 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
- Balance performance, scalability, cost, reliability, security, privacy, maintainability, and speed of delivery
- Apply sound engineering judgment when evaluating technical approaches and system architecture decisions
- Strengthen model evaluation, observability, reproducibility, lineage, security, and operational resilience
- Implement practices that support responsible and trustworthy AI deployment within scientific environments
- 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
- Define success metrics and operational indicators
- Continuously improve performance, reliability, usability, and scientific impact
- Help teams adopt AI with confidence and scale
- PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical discipline with demonstrated experience building software, systems, or machine learning solutions with 2+ years of relevant experience; OR
- Master's degree with 5+ years of relevant experience; OR
- Bachelor's degree with 6+ years of relevant experience
- Designing and delivering production software or machine learning systems
- Owning technical solutions from architecture through deployment
- Building reliable systems in cloud, distributed computing, or HPC environments
- Developing maintainable, testable, and scalable software
- Python software development
- Modern machine learning frameworks such as PyTorch or JAX
- Software architecture and system design
- APIs, testing, debugging, version control, and technical documentation
- Containers, orchestration, CI/CD, and deployment workflows
- Distributed GPU training
- Model serving and inference optimization
- AI platforms and MLOps
- Data and knowledge systems
- LLM and agentic architectures
- Retrieval systems and RAG
- AI evaluation frameworks
- Performance and infrastructure optimization
- Work effectively across interdisciplinary teams
- Navigate ambiguity and solve complex problems
- Communicate technical concepts to broad audiences
- Influence decisions through expertise and execution
- Support and develop less-experienced teammates
- Experience in life sciences, healthcare, biotechnology, or highly regulated industries
- Experience with multimodal AI, scientific machine learning, computational biology, or cheminformatics
- Experience implementing responsible AI practices, evaluation frameworks, and governance controls
- Evidence of technical impact through deployed systems, publications, patents, open-source contributions, or reusable engineering platforms
- Take ownership and consistently deliver
- Balance innovation with engineering discipline
- Embrace difficult technical challenges
- Learn quickly and continuously
- Build trust through execution and collaboration
- Help elevate the capabilities of the broader team
- Stay focused on outcomes and scientific impact
- Meaningful ownership
- Complex technical challenges
- Exceptional collaborators
- Significant opportunities for growth
- The ability to build AI with real-world impact
$197.2k - $266.8k
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