GSK
Staff, Full Stack Engineer
USA - Pennsylvania - Philadelphia +1 · Hybrid (Remote & On-site) · Staff · Full-time · Posted Oct 26, 2026
- Location
- USA - Pennsylvania - Philadelphia
Durham Blackwell Street - Experience
- 10+ years.
- Education
- Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
About the job
Sets technical direction and architecture for GSK Enterprise AI’s full-stack AI products, agents, platforms, and governed context integrations, while establishing engineering standards and developing senior engineers.
What you'll do
- Lead multi-quarter technical strategy and major AI product initiatives across modelling, platforms, APIs, and interfaces.
- Define architecture and standards for agent-operated engineering, AI integration, APIs, frontend systems, LLMOps, evaluation, and observability.
- Establish agentic research and AI-assisted development practices, lead foundational platform investments, and coach senior engineers.
What they ask for
- Bachelor's degree in a relevant quantitative field or equivalent practical experience.
- 10+ years of experience in applied machine learning or data science and full-stack engineering, including building and operating user-facing AI products.
- Experience across the AI product stack, including production AI integration, API/platform/frontend standards, and LLMOps evaluation and monitoring infrastructure.
- Experience scaling agentic research and product workflows and setting multi-quarter technical strategy across multiple engineering teams.
Nice to have
- Master's or PhD in a relevant quantitative field, or equivalent practical experience.
- Prior Staff-level experience shipping AI products combining modelling and full-stack delivery; regulated-industry experience is also valued.
Tools
LLM, Model Context Protocol (MCP), LLMOps
AI focus
Leads production AI product and agent architecture, including model selection, orchestration, retrieval and context integration, agentic research and development, AI-native interfaces, evaluation, observability, governance, and system health.
Model-written summary · the original posting is authoritative