Staff, Full Stack Engineer

GSK•Philadelphia, PA

2d•Hybrid

About The Position

The Staff Full Stack Engineer sets technical direction for the AI product stack across GSK’s Enterprise AI (EAI) organization. EAI builds and ships production AI products that combine modeling, platforms, APIs, data, and user-facing applications. Engineers establish the architecture and standards used by both people and digital workers, and are accountable for the quality of what ships. The role owns the integration and orchestration architecture through which EAI products and agents use the governed context layer, and serves as EAI’s internal authority on how AI capabilities reach users. The role sets multi-quarter technical direction across modeling workflows, platforms and APIs, AI-powered interfaces, and agentic systems. It defines how digital workers may generate and change engineering work, the human approval requirements and evaluation gates, and how incidents are escalated. It also establishes EAI’s agentic research and development practice, including continuous experimentation, auto-research, evolving production models, and agent-driven application development. The Staff Full Stack Engineer defines API, platform, frontend, full-stack, LLMOps, evaluation, observability, and governance standards used across EAI teams. The role shapes AI integration patterns, including model selection, orchestration, retrieval, tool use, and context access over protocols such as MCP. It leads the development of AI-native user experiences, production system health practices, and foundational shared investments such as evaluation frameworks, agent runtimes, experiment management, internal SDKs, and shared components. The role also develops senior engineers and partners with research, product, design, data engineering, and platform teams. In the first 12 months, success includes setting and beginning to execute a multi-quarter AI product-stack strategy; establishing standards and an operating model for engineers and digital workers; leading a platform investment that changes how teams build; putting context integration, evaluation, and observability standards into production; defining agentic research and AI-assisted development practices with measurable adoption and impact; and increasing senior engineers’ architectural independence. This is a hybrid role based in the United States. Scheduled on-site time is required; fully remote arrangements are not available.

Requirements

Bachelor’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field, or equivalent practical experience.
At least 10 years of experience in applied machine learning or data science and full-stack engineering, including experience building and operating user-facing AI products.
Experience across the AI product stack, including modeling pipelines, evaluations, platforms and APIs, modern web frameworks, and data and cloud infrastructure.
Experience designing and operating production AI products, including LLM selection and integration, orchestration and tool-use architectures, retrieval and context integration using protocols such as MCP, safety, and cost, latency, and quality optimization.
Experience defining API, platform, and frontend engineering standards for multiple teams, including interfaces designed for non-deterministic, agent-driven behavior.
Experience building LLMOps, evaluation, and monitoring infrastructure for production AI systems.
Experience defining and scaling agentic research and product workflows, such as auto-research, continuous evaluation, and agent-driven application development.
Experience setting multi-quarter technical strategy across multiple engineering teams.

Nice To Haves

Master’s or PhD in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field, or equivalent practical experience.
Prior Staff-level or equivalent experience at an organization shipping substantial AI products combining modeling and full-stack delivery.
Experience defining organization-wide engineering strategy, particularly for agentic research and product workflows, and delivering measurable outcomes.
Experience in a regulated industry, such as life sciences, where data integrity and auditability are mandatory.
Public technical contributions, such as talks, writing, or open-source work, on AI-native or agent-augmented engineering.
Experience defining API and platform architecture standards for multiple engineering teams and making platform decisions that improve reliability and developer productivity.
Experience with AI integration architecture, including LLM selection, orchestration patterns, and LLMOps, and implementing evaluation pipelines and AI observability practices across multiple teams.
Experience defining frontend engineering standards for AI products and designing non-deterministic, agent-driven user interfaces.
Experience defining LLMOps standards for AI system observability, evaluation, and governance, and building production AI evaluation and monitoring infrastructure.
Experience defining full-stack engineering standards, including standards for agent-generated code.
Experience developing developer tooling and shared component strategies.

Responsibilities

Set multi-quarter technical direction for the AI product stack across EAI and lead ambitious end-to-end initiatives that turn high-stakes problems into shipped systems.
Define EAI’s agentic research practice for continuous experimentation, auto-research, evolving production models, and agent-driven research and application iteration.
Set the operating model for agent-generated engineering work, including what digital workers may build or change independently, human approval requirements, evaluation gates, and incident escalation.
Set AI-assisted development standards, tooling, workflows, and practices for coding and research agents, with measurable effects on velocity and quality.
Define API and platform architecture standards across EAI engineering teams, including contracts, authentication, versioning, and non-functional requirements.
Drive architectural coherence across modeling pipelines, evaluations, retrieval, agent orchestration, application services, frontends, data, and observability without becoming a bottleneck.
Shape platform decisions that improve reliability and developer productivity.
Shape EAI’s AI integration architecture, including LLM selection, orchestration patterns, retrieval, tool use, and LLMOps standards.
Own how EAI products and agents access the governed context layer over standard protocols such as MCP, establishing consistent retrieval, permissions, and provenance patterns.
Define frontend engineering standards and shape how AI capabilities reach users across the portfolio.
Lead EAI’s approach to AI-native UX, including interfaces for non-deterministic, agent-driven behavior with confidence signals, error states, provenance, and human-override paths.
Define LLMOps standards for production AI observability, evaluation, and governance, and lead enterprise-scale evaluation and monitoring infrastructure.
Own long-term production AI system health across reliability, performance, cost, security, model behavior, safety, and abuse.
Define full-stack engineering standards across EAI pods for both people and digital workers.
Shape developer tooling and shared component strategy, leading foundational investments such as evaluation frameworks, agent runtimes, experiment management, internal SDKs, and shared components.
Develop senior engineers toward greater architectural independence and partner with research, product, design, data engineering, and platform teams on challenging product problems.