Staff, Full Stack Engineer

Description

Enterprise AI (EAI) builds AI products that pair real modelling work with user-facing applications and runs them in production. Full Stack Engineers work across data, models, platforms, APIs and interfaces within one workflow.

Digital workers do much of the hands-on build here: they generate services, APIs, interface components and tests, run evaluations and watch what is live. Engineers set the architecture and standards that this work follows and are responsible for the quality of what ships.

EAI products and agents rely on a governed context layer that makes data, meaning, permissions and history available when they need them. Full Stack Engineers build the products and agents that use this layer and decide how that context gets to users.

The Staff Full Stack Engineer sets technical direction for the AI product stack across EAI. This person owns the integration and orchestration architecture that connects EAI products and agents to the governed context layer, and is the internal reference for how AI capabilities reach users.

Responsibilities

• Set technical direction for the AI product stack across EAI over several quarters, covering modelling workflows, platforms, APIs, AI-powered interfaces and the agentic systems linking them.
• Lead the most ambitious end-to-end efforts, turning unclear, high-stakes problems into systems that ship and last.
• Build EAI's agentic research practice around continuous experimentation and auto-research, improving production models and using agents to drive research and application iteration.
• Define the operating model for agent-generated engineering: what digital workers can build and change alone, what needs human sign-off, which evaluation gates their output must pass and how incidents escalate.
• Set the standard for AI-assisted development, including tooling, workflows and practices for coding and research agents, and measure their effect on velocity and quality.
• Define API and platform architecture standards for all EAI engineering teams, covering contracts, authentication, versioning and non-functional requirements.
• Keep modelling pipelines, evaluations, retrieval, agent orchestration, application services, frontends, data and observability architecturally coherent without becoming a blocker. Make platform decisions that measurably 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 use the governed context layer through standard protocols such as MCP, with consistent patterns for retrieval, permissions and provenance.
• Define EAI's frontend engineering standards and decide how AI capabilities reach users across the portfolio.
• Lead EAI's approach to AI-native UX for non-deterministic, agent-driven interfaces, including confidence signals, error states, provenance and human override paths that build user trust.
• Define EAI's LLMOps standards for observability, evaluation and governance of production AI systems, and lead evaluation and monitoring infrastructure at enterprise scale.
• Own long-term health of production AI systems across reliability, performance, cost, security, model behaviour, safety and abuse, acting as EAI's authority on that health.
• Define full-stack engineering standards for all EAI pods, written so that people and digital workers can both follow them.
• Shape EAI's developer tooling and shared component strategy, leading foundational work such as evaluation frameworks, agent runtimes, experiment management, internal SDKs and shared components that cut duplication and change how teams build.
• Grow senior engineers toward greater architectural independence, and work with research, product, design, data engineering and platform teams on the hardest product problems.

Requirements

• Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
• 10+ years in applied machine learning or data science and full-stack engineering, including building and running user-facing AI products.
• Experience across the AI product stack: modelling pipelines, evaluations, platforms and APIs, modern web frameworks, and data and cloud infrastructure.
• Experience designing and running production AI products, including LLM selection and integration, orchestration and tool-use architectures, retrieval and context integration using protocols such as Model Context Protocol (MCP), safety, and optimising cost, latency and quality.
• Experience defining API, platform and frontend standards for several teams, including interfaces built for non-deterministic, agent-driven behaviour.
• Experience building LLM 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 several engineering teams.
• Preferred: Master's or PhD strongly preferred in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
• Preferred: Prior Staff, or equivalent, experience at an organisation shipping substantial AI products that combine modelling and full-stack delivery.
• Preferred: Set organisation-wide engineering strategy, particularly for agentic research and product workflows, and carried it through to measurable results.
• Preferred: Experience in a regulated industry, such as life sciences, where data integrity and auditability are required.
• Preferred: Public technical work, such as talks, writing or open source, on AI-native, agent-augmented engineering.
• Preferred: Experience defining API and platform architecture standards for several engineering teams and making platform decisions that improve reliability and developer productivity.
• Preferred: Experience with AI integration architecture, including LLM selection, orchestration patterns and LLMOps, and building evaluation pipelines and AI observability across several teams.
• Preferred: Experience defining frontend standards for AI products and designing non-deterministic, agent-driven interfaces.
• Preferred: Experience defining LLMOps standards, including observability, evaluation and governance, and building evaluation and monitoring infrastructure for production AI systems.
• Preferred: Experience defining full-stack standards, including standards for agent-generated code.
• Preferred: Experience developing developer tooling and shared component strategies.
• In the first 12 months you will have set and started running a multi-quarter technical strategy for the AI product stack across EAI.
• In the first 12 months you will have set the operating model and standards for building across platforms, APIs and interfaces, and led at least one platform investment that changed how teams build.
• In the first 12 months you will have defined the integration and orchestration architecture connecting EAI products and agents to the governed context layer, with evaluation and observability standards in production.
• In the first 12 months you will have defined EAI's approach to agentic research and AI-assisted development, with measurable adoption and effect on velocity and quality.
• In the first 12 months you will have raised the architectural independence of EAI's senior engineers through standards and coaching.

Conditions

Hybrid role based in the United States, with scheduled time on-site to work with your team. Fully remote arrangements are not available.

Equal-opportunity and legal notices, including the company's policy on referrals from employment agencies, apply to this role.

Benefits

• The company's US benefits programme is described on its benefits summary page.