{"id":3323257,"url":"https://alion.io/job/gsk-staff-full-stack-engineer-2","title":"Staff, Full Stack Engineer","company":{"id":6649,"name":"GSK","domain":"gsk.com","url":"https://alion.io/company/gsk","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":86,"open_postings":58,"ghost_share":0,"stale_share":0.759,"repost_share":0,"time_to_fill_p50_days":17,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Philadelphia, United States","Durham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":225000,"max_usd":419000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1500},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false}],"status":"live","first_seen_at":"2026-10-26T00:00:00Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-31T15:21:53Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"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 workflow. In 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.\nEAI 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.\nThe 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 EAI's internal authority on how AI capabilities reach users.\nSets 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.\nOwns 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.\nDefines EAI's agentic research and development practice for continuous experimentation, auto-research and agent-driven application development, and develops the senior engineers who carry it forward.\nKey Responsibilities:\nTechnical Strategy & Research Practice\nSet 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.\nDefine EAI's agentic research practice for continuous experimentation and auto-research, evolving production models, and using agents to drive research and application iteration.\nAgent-Operated Engineering\nSet 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.\nSet the AI-assisted development standard: the tooling, workflows and practices for coding and research agents, with measurable impact on velocity and quality.\nPlatforms & APIs\nDefine API and platform architecture standards adopted across EAI engineering teams, covering contracts, authentication, versioning and non-functional requirements.\nDrive 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.\nAI Integration & Context\nShape EAI's AI integration architecture: LLM selection, orchestration patterns, retrieval and tool use, and LLMOps standards.\nOwn 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.\nAI-Native Experience\nDefine EAI's frontend engineering standards and shape how AI capabilities reach users across the portfolio.\nLead EAI's 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.\nLLMOps & System Health\nDefine EAI's LLMOps standards for observability, evaluation and governance of production AI systems, and lead enterprise-scale evaluation and monitoring infrastructure.\nOwn long-term system health across reliability, performance, cost, security, model behaviour, safety and abuse, as EAI's authority on production AI system health.\nStandards & Technical Leadership\nDefine full-stack engineering standards adopted across EAI pods, written to be followed by humans and digital workers alike.\nShape EAI's 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.\nDevelop senior engineers towards greater architectural independence, and partner with research, product, design, data engineering and platform teams on the hardest product problems.\nWhy You?\nBasic Qualification \nWe are seeking professionals with the following required skills and qualifications to help us achieve our goals:\nBachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.\n 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.\nExperience across the AI product stack, including modelling pipelines, evaluations, platforms and APIs, modern web frameworks, and data and cloud infrastructure.\nExperience 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.\nExperience defining API, platform and frontend engineering standards for multiple teams, including interfaces designed for non-deterministic, agent-driven behaviour.\nExperience building large language model operations (LLMOps), evaluation and monitoring infrastructure for production AI systems.\nExperience defining and scaling agentic research and product workflows, such as auto-research, continuous evaluation and agent-driven application development.\nExperience setting multi-quarter technical strategy across multiple engineering teams.\n\nPreferred Qualification\nIf you have the following characteristics, it would be a plus: \nMaster's or PhD strongly preferred, in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.\nPrior Staff, or equivalent, experience at an organisation shipping non-trivial AI products that combine modelling and full-stack delivery.\nDefined organisation-wide engineering strategy, particularly for agentic research and product workflows, and seen it through to measurable outcomes.\nExperience in a regulated industry, such as life sciences, where data integrity and auditability are mandatory.\nPublic technical contributions, such as talks, writing or open source, on AI-native, agent-augmented engineering\nExperience defining API and platform architecture standards for multiple engineering teams and making platform decisions to improve reliability and developer productivity.\nExperience 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.\nExperience defining frontend engineering standards for AI products and designing non-deterministic, agent-driven user interfaces.\nExperience defining LLMOps standards, including AI system observability, evaluation and governance and building evaluation and monitoring infrastructure for production AI systems.\nExperience defining full-stack engineering standards, including standards for agent-generated code.\nExperience developing developer tooling and shared component strategies.\nWhat Success Looks Like\nIn the first 12 months, the person in this role will have:\nSet and begun executing a multi-quarter technical strategy for the AI product stack across EAI.\nEstablished 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.\nDefined the integration and orchestration architecture through which EAI products and agents draw on the governed context layer, with evaluation and observability standards in production.\nDefined EAI's approach to agentic research and AI-assisted development, with measurable adoption and impact on velocity and quality.\nRaised the architectural independence of EAI's senior engineers through standards and coaching.\nWork Arrangement \nThis 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.\n#Li-GSK\nSkills\nAI Platforms, Change Management, Data Analysis, Feature Prioritization, Machine Learning Model Management, ML Platforms, Model Evaluation, Performance Analysis, Product Delivery, Product Design, Product Lifecycle Management (PLM), Product Roadmapping, Roadmap Development, Statistical Analysis, User Needs AnalysisPlease visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.\nWhy GSK?\nUniting science, technology and talent to get ahead of disease together.\nGSK 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.\nPeople and patients around the world count on the medicines and vaccines we make, so we’re 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.\nIf you require an accommodation or other assistance to apply for a job at GSK, please contact the appropriate Recruitment Staff by emailing us at - \nGSK is an Equal Opportunity Employer. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), military service or any basis prohibited under federal, state or local law.\nImportant notice to Employment businesses/ Agencies\nGSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site.\nPlease note that if you are a US Licensed Healthcare Professional or Healthcare Professional as defined by the laws of the state issuing your license, GSK may be required to capture and report expenses GSK incurs, on your behalf, in the event you are afforded an interview for employment. 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