AI Engineer

• Serve as the senior software and AI technical expert for Cell & Genetic Medicines high-throughput screening capabilities
• Define and implement scalable architecture connecting laboratory automation, analytical instruments, enterprise systems, scientific data, and user workflows
• Build applications, APIs, data pipelines, computational tools, and user-facing visualization solutions for cell therapy discovery
• Develop and deploy AI-enabled capabilities, including machine learning, generative AI, retrieval-augmented generation, and agentic workflows
• Identify and prioritize software and AI opportunities to improve organizational effectiveness, scientific productivity, operational efficiency, and decision-making
• Translate experimental processes and scientific questions into intuitive, maintainable, and reproducible digital solutions
• Organize and contextualize screening data, construct information, assay metadata, and analytical outputs
• Improve experimental planning, data quality, hit identification, candidate ranking, and knowledge capture
• Establish engineering best practices for architecture, development, testing, documentation, version control, deployment, observability, security, and lifecycle management
• Build cross-functional partnerships across Discovery Research, IT, Business Technology, and other groups
• Align stakeholders, clarify expectations, and communicate decisions, risks, tradeoffs, and progress
• Influence technical direction without direct authority
• Mentor scientists and engineers in software engineering, data architecture, and responsible AI use
• Travel approximately 10–15% to company sites, including Spring House, Pennsylvania, La Jolla, California, and Cambridge, Massachusetts

Principal Engineer, AI/Software Support

• Serve as the senior software and AI technical expert for Cell & Genetic Medicines high-throughput screening capabilities
• Define and implement scalable architecture connecting laboratory automation, analytical instruments, enterprise systems, scientific data, and user workflows
• Build applications, APIs, data pipelines, computational tools, and user-facing visualization solutions for cell therapy discovery
• Develop and deploy AI-enabled capabilities, including machine learning, generative AI, retrieval-augmented generation, and agentic workflows
• Identify and prioritize software and AI opportunities that improve organizational effectiveness, scientific productivity, operational efficiency, and decision-making
• Translate experimental processes and scientific questions into intuitive, maintainable, and reproducible digital solutions
• Organize and contextualize screening data, construct information, assay metadata, and analytical outputs
• Improve experimental planning, data quality, hit identification, candidate ranking, and knowledge capture
• Establish engineering best practices for architecture, development, testing, documentation, version control, deployment, observability, security, and lifecycle management
• Build cross-functional and cross-departmental partnerships across Discovery Research, IT, Business Technology, and other groups
• Align stakeholders, clarify expectations, and communicate decisions, risks, tradeoffs, and progress
• Influence technical direction without direct authority
• Mentor scientists and engineers in software engineering, data architecture, and responsible AI use
• Create a connected, data-driven screening environment that supports reproducible experiments and therapeutic candidate selection

Principal Engineer, AI/Software Support, Pooled Screening Core

• Provide tailored guidance to business units on AI/ML use cases, feasibility, model selection, and deployment options
• Co-design prototypes and proof-of-concepts with product and domain teams
• Translate stakeholder requirements into scoped technical solutions with success criteria and handover plans
• Build, train, evaluate, and iterate on machine learning models for scientific and business problems
• Package trained models into production-ready APIs and containerized deployments using GSK’s cloud infrastructure
• Develop and maintain agentic AI systems, multi-agent architectures, and LLM-based tools
• Share reusable patterns, baseline models, and tested pipelines for common AI/ML tasks
• Embed privacy, ethics, and regulatory considerations into engagements
• Run workshops, seminars, and hands-on training sessions to increase AI literacy
• Embed within business/research units for typically 6–8 week engagements to accelerate delivery and transfer skills
• Communicate issues, requests, and opportunities from business units to AI/ML product leads

AI Engineer jobs in Pennsylvania

Principal Engineer, AI/Software Support

Principal Engineer, AI/Software Support, Pooled Screening Core

Applied AI Engineer