Senior Principal AI Research Engineer

Pfizer

. Build, design, develop, and deploy production-grade AI systems supporting multimodal biomedical models and intelligent agents .

Posted 11/6/2026full-timeUnited StatesSenior💰 $139,100 - $231,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying production-grade AI systems, with a strong focus on software architecture, machine learning frameworks, and interdisciplinary collaboration. Proven ability to translate scientific needs into technical solutions while ensuring system reliability, scalability, and performance.

Highest-signal resume keywords

PhD In Computer Science Or Related FieldStrong Proficiency In Python Software DevelopmentExperience With Modern Machine Learning Frameworks (PyTorch, JAX)Experience Building Reliable Systems In Cloud Or Distributed ComputingProficiency With APIs, Testing, Debugging, And CI/CD

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Hard Skills

Software ArchitectureMachine Learning SystemsData PipelinesInference ServicesModel Evaluation FrameworksDistributed GPU TrainingAI Platforms And MLOpsTesting And DebuggingTechnical DocumentationPerformance Optimization

Soft Skills

Interdisciplinary CollaborationProblem SolvingTechnical CommunicationMentoring And CoachingInfluencing Decisions

Tools & Technologies

APIsContainersOrchestrationCI/CD WorkflowsVersion Control

Industry Keywords

Life SciencesHealthcareBiotechnologyMultimodal AIScientific Machine LearningComputational BiologyResponsible AI PracticesGovernance Controls

Tech Stack

Tools & technologies

CloudPythonPyTorch

About the role

Key responsibilities & impact

Build, 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 moves AI capabilities from experimentation into everyday scientific use
Own an end-to-end subsystem, platform capability, or workflow
Translate scientific and business needs into technical solutions
Lead architecture, implementation, testing, deployment, monitoring, and continuous improvement
Drive projects from concept through production and operational support
Partner with AI scientists, computational biologists, data engineers, and platform teams
Transform prototypes into reusable, reliable, and maintainable systems
Balance performance, scalability, cost, reliability, security, privacy, maintainability, and delivery speed
Strengthen model evaluation, observability, reproducibility, lineage, security, and operational resilience
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 technical coaching to less-experienced engineers
Define success metrics and operational indicators
Continuously improve performance, reliability, usability, and scientific impact
Help teams adopt AI with confidence and scale

Requirements

What you’ll need

PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical discipline 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
Demonstrated experience designing and delivering production software or machine learning systems
Experience owning technical solutions from architecture through deployment
Experience building reliable systems in cloud, distributed computing, or HPC environments
Experience developing maintainable, testable, and scalable software
Strong proficiency in Python software development
Proficiency with modern machine learning frameworks such as PyTorch or JAX
Proficiency in software architecture and system design
Proficiency with APIs, testing, debugging, version control, and technical documentation
Proficiency with containers, orchestration, CI/CD, and deployment workflows
Depth in one or more of: 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
Ability to work effectively across interdisciplinary teams
Ability to navigate ambiguity and solve complex problems
Ability to communicate technical concepts to broad audiences
Ability to influence decisions through expertise and execution
Ability to support and develop less-experienced teammates
Candidates must be authorized to be employed in the U.S. by any employer
Permanent work authorization in the United States required
U.S. work visa sponsorship is not available now or in the future
Preferred: experience in life sciences, healthcare, biotechnology, or highly regulated industries
Preferred: experience with multimodal AI, scientific machine learning, computational biology, or cheminformatics
Preferred: experience implementing responsible AI practices, evaluation frameworks, and governance controls
Preferred: evidence of technical impact through deployed systems, publications, patents, open-source contributions, or reusable engineering platforms

Benefits

Comp & perks

Global Performance Plan with a bonus target of 17.5% of base salary
Share-based long-term incentive program
401(k) plan with Pfizer Matching Contributions
Additional Pfizer Retirement Savings Contribution
Paid vacation, holiday and personal days
Paid caregiver/parental and medical leave
Medical, prescription drug, dental and vision coverage
Relocation assistance may be available based on business needs and/or eligibility