Staff, Full Stack Engineer

Enterprise AI (EAI) builds AI products that combine real modelling work with user-facing applications and ships them to production. Full Stack Engineers move across data models platforms APIs and interfaces in a single our operating model digital workers do much of the hands-on build. They generate services APIs interface components and tests run evaluations and monitor what is in production. Our engineers set the architecture and standards that work is built to and own the quality of what ships.

EAI products and agents draw on a governed context layer that makes data meaning permissions and history retrievable at the moment they are needed. Full Stack Engineers build the products and agents that consume it and decide how that context reaches users.

The Staff Full Stack Engineer sets technical direction for the AI product stack across EAI. The role is accountable for the integration and orchestration architecture through which EAI products and agents draw on the governed context layer and is EAIs internal authority on how AI capabilities reach users.

• Sets multi-quarter technical direction for the AI product stack across EAI: modelling workflows platforms and APIs AI-powered interfaces and the agentic systems that connect them.

• Owns the integration and orchestration architecture through which EAI products and agents draw on the governed context layer and defines the standards engineers and digital workers build to.

• Defines EAIs agentic research and development practice for continuous experimentation auto-research and agent-driven application development and develops the senior engineers who carry it forward.

• Set multi-quarter technical direction for the AI product stack across EAI and lead the most ambitious end-to-end bets turning vague high-stakes problems into shipped systems with durable impact.

• Define EAIs agentic research practice for continuous experimentation and auto-research evolving production models and using agents to drive research and application iteration.

• Set the operating model for agent-generated engineering work: what digital workers build and change on their own what requires human approval which evaluation gates their work must pass and how incidents escalate.

• Set the AI-assisted development standard: the tooling workflows and practices for coding and research agents with measurable impact on velocity and quality.

• Define API and platform architecture standards adopted across EAI engineering teams covering contracts authentication versioning and non-functional requirements.

• Drive architectural coherence across modelling pipelines evaluations retrieval agent orchestration application services frontends data and observability without becoming a bottleneck. Shape platform decisions that measurably improve reliability and developer productivity.

• Shape EAIs AI integration architecture: LLM selection orchestration patterns retrieval and tool use and LLMOps standards.

• Own how EAI products and agents draw on the governed context layer over standard protocols such as MCP with consistent patterns for retrieval permissions and provenance.

• Define EAIs frontend engineering standards and shape how AI capabilities reach users across the portfolio.

• Lead EAIs approach to AI-native UX: designing for non-deterministic agent-driven interfaces with confidence signals error states provenance and human-override paths that earn user trust.

• Define EAIs LLMOps standards for observability evaluation and governance of production AI systems and lead enterprise-scale evaluation and monitoring infrastructure.

• Own long-term system health across reliability performance cost security model behaviour safety and abuse as EAIs authority on production AI system health.

• Define full-stack engineering standards adopted across EAI pods written to be followed by humans and digital workers alike.

• Shape EAIs developer tooling and shared component strategy leading foundational investments such as evaluation frameworks agent runtimes experiment management internal SDKs and shared components that reduce duplication and change how teams build.

• Develop senior engineers towards greater architectural independence and partner with research product design data engineering and platform teams on the hardest product problems.

Why You
Basic Qualification
We are seeking professionals with the following required skills and qualifications to help us achieve our goals:

• Bachelors degree in Computer Science Data Science Statistics Applied Mathematics or a related quantitative field or equivalent practical experience.
• 10 years of experience in applied machine learning (ML) or data science and full-stack engineering including experience building and operating user-facing AI products.
• Experience across the AI product stack including modelling pipelines evaluations platforms and APIs modern web frameworks and data and cloud infrastructure.
• Experience designing and operating production AI products including large language model (LLM) selection and integration orchestration and tool-use architectures retrieval and context integration using protocols such as Model Context Protocol (MCP) safety and cost latency and quality optimisation.
• Experience defining API platform and frontend engineering standards for multiple teams including interfaces designed for non-deterministic agent-driven behaviour.
• Experience building large language model operations (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.

Preferred Qualification
If you have the following characteristics it would be a plus:

• Masters or PhD strongly preferred in Computer Science Data Science Statistics Applied Mathematics or a related quantitative field or equivalent practical experience.
• Prior Staff or equivalent experience at an organisation shipping non-trivial AI products that combine modelling and full-stack delivery.

• Defined organisation-wide engineering strategy particularly for agentic research and product workflows and seen it through to 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 agent-augmented engineering
• Experience defining API and platform architecture standards for multiple engineering teams and making platform decisions to improve reliability and developer productivity.
• Experience with AI integration architecture including large language model (LLM) selection orchestration patterns and LLM operations (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 including AI system observability evaluation and governance and building evaluation and monitoring infrastructure for production AI systems.
• Experience defining full-stack engineering standards including standards for agent-generated code.
• Experience developing developer tooling and shared component strategies.
  
  

In the first 12 months the person in this role will have:

• Set and begun executing a multi-quarter technical strategy for the AI product stack across EAI.

• Established the operating model and standards under which engineers and digital workers build across platforms APIs and interfaces and led at least one platform investment that changed how teams build.

• Defined the integration and orchestration architecture through which EAI products and agents draw on the governed context layer with evaluation and observability standards in production.

• Defined EAIs approach to agentic research and AI-assisted development with measurable adoption and impact on velocity and quality.

• Raised the architectural independence of EAIs senior engineers through standards and coaching.

Work Arrangement
This role is hybrid and based in the United States. You will spend scheduled time on-site to collaborate and engage with your team. Fully remote arrangements are not available.

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Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.

Why GSK

Uniting science technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade as a successful growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory immunology and inflammation; oncology; HIV; and infectious diseases to impact health at scale.

People and patients around the world count on the medicines and vaccines we make so were committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients accountable for impact and doing the right thing is the foundation for how together we deliver for patients shareholders and our people.

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