Staff, Full Stack Engineer

GSK•Philadelphia, PA

2d•Hybrid

About The Position

Enterprise AI (EAI) builds AI products that combine modeling with user-facing applications and brings them into production. The Staff Full Stack Engineer sets technical direction for EAI’s AI product stack and is accountable for the integration and orchestration architecture through which products and agents use the governed context layer. The role serves as EAI’s internal authority on how AI capabilities reach users. The engineer sets multi-quarter direction across modeling workflows, platforms and APIs, AI-powered interfaces, and agentic systems. They establish standards for work performed by engineers and digital workers, define EAI’s agentic research and development practice, and lead ambitious end-to-end initiatives from complex problems through production systems with durable impact. The role shapes API, platform, AI integration, frontend, and LLMOps architecture across EAI. It establishes standards for production AI system evaluation, observability, governance, reliability, security, safety, performance, and cost. The Staff Full Stack Engineer also leads foundational developer-tooling and shared-component investments and develops senior engineers’ architectural independence, partnering with research, product, design, data engineering, and platform teams.

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 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 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 that combine 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, 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, LLMOps, 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 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.
Define EAI’s agentic research practice, including continuous experimentation, auto-research, evolving production models, and agent-driven research and application iteration.
Establish the operating model for agent-generated engineering work, including what digital workers may build independently, human approval requirements, evaluation gates, and incident escalation.
Set AI-assisted development standards for agent tooling, workflows, and practices, with measurable effects on velocity and quality.
Define API and platform architecture standards across EAI 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 while avoiding becoming a bottleneck.
Shape platform decisions that measurably improve reliability and developer productivity.
Define AI integration architecture, including LLM selection, orchestration patterns, retrieval, tool use, and LLMOps standards.
Own consistent patterns for how products and agents use the governed context layer over protocols such as MCP, including retrieval, permissions, and provenance.
Define frontend engineering standards and shape how AI capabilities reach users across the portfolio.
Lead EAI’s approach to AI-native user experience, including confidence signals, error states, provenance, and human-override paths for non-deterministic, agent-driven interfaces.
Define LLMOps standards for observability, evaluation, and governance of production AI systems, 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 for EAI pods that can be followed by both people and digital workers.
Shape developer tooling and shared-component strategy, including investments in 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 difficult product problems.