Senior AI Research Engineer

Job description

Help Build a New AI-Native Operating ModelAt 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 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; OR Master's degree with 2+ years of relevant experience; OR Bachelor's degree with 4+ years of relevant experience Technical Expertise You have demonstrated 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 Engineering Proficiency You bring strong proficiency in: 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 You have depth in one or more of the following areas: 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 Collaboration & Leadership You have a proven ability to: 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 Preferred Qualifications 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 How You'll Succeed The most successful engineers in this role: 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 Why Pfizer AI is becoming a foundational capability across Pfizer's R&D organization. We are building an environment where exceptional engineers can tackle technically ambitious challenges, work with world-class scientific experts, and contribute to innovations that have the potential to improve lives around the world. Here, you'll find: Meaningful ownership Complex technical challenges Exceptional collaborators Significant opportunities for growth The ability to build AI with real-world impact If you want to build production AI at scale while helping shape the future of scientific discovery, this i