Senior Principal AI Research Engineer

Pfizer

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

Posted 11/7/2026full-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 lead projects from concept to operational support while ensuring system reliability, scalability, and performance.

Highest-signal resume keywords

PhD In Computer ScienceStrong Proficiency In Python Software DevelopmentExperience With Modern Machine Learning FrameworksExperience Building Reliable Systems In Cloud EnvironmentsProficiency With APIs And CI/CD Workflows

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software ArchitectureMachine Learning SolutionsData PipelinesDistributed ComputingTesting And DebuggingModel Serving And Inference OptimizationPerformance OptimizationTechnical DocumentationContainers And OrchestrationKnowledge Systems

Soft Skills

Interdisciplinary CollaborationProblem SolvingTechnical CommunicationMentorshipInfluencing Decisions

Tools & Technologies

PyTorchJAXAPIsCI/CDVersion ControlHPC EnvironmentsAI PlatformsMLOpsRetrieval SystemsHuman-In-The-Loop Workflows

Industry Keywords

Biomedical ModelsAI SystemsProduction SoftwareOperational ResilienceModel Evaluation Frameworks

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
Make architecture and design tradeoff decisions
Lead architecture, implementation, testing, deployment, monitoring, and continuous improvement
Drive projects from concept through production and operational support
Translate scientific and business needs into technical solutions
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
Guide and support less-experienced engineers through collaboration, code reviews, technical coaching, and mentorship
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 demonstrated experience building software, systems, or machine learning solutions and 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, navigate ambiguity, solve complex problems, communicate technical concepts to broad audiences, influence decisions, and support less-experienced teammates
Candidates must be authorized to be employed in the U.S. by any employer
U.S. work visa sponsorship is not available now or in the future
This position requires permanent work authorization in the United States

Benefits

Comp & perks

Global Performance Plan 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
Hybrid work arrangement