Senior, Full Stack Engineer

The Senior Full Stack Engineer leads the design and delivery of a significant AI product area, spanning modelling workflows and user-facing applications. The role sets the team's standards for APIs, AI integration, interfaces and observability, designs the agentic research systems that drive continuous iteration, and mentors junior and mid-level engineers.

Key Responsibilities

Technical Leadership & Research Practice

• Set technical direction for the product area and its modelling pipelines, defining boundaries, evaluation strategies and defensible trade-offs on performance, reliability and cost.
• Design agentic research systems: auto-research agents that run experiments and synthesise findings, agents that monitor and improve production models, and agents that scaffold and iterate on features.
• Partner with product, design and research to shape what gets built, challenging weak ideas and communicating trade-offs clearly.

Agent-Operated Engineering

• Define the team's patterns for agent-generated work within EAI's operating model: what digital workers build and change on their own, which evaluation gates their work must pass, and when it needs human review.
• Set the team's AI-assisted development patterns for coding and research agents, establishing safe, effective use the team adopts.

Platforms & APIs

• Lead the design of API layers that serve multiple consumers, defining integration contracts and non-functional requirements for the team.
• Set or improve API design standards used across the product area, and guide others on backend architecture decisions.
• Lead the creation of shared infrastructure and tooling that materially reduces integration cost across multiple teams, and drive its adoption.

AI Integration & Context Design

• AI orchestration architectures at production scale: agent frameworks, multi-step workflows, retrieval, tool use, guardrails and integration patterns.
• Define the team's standards for AI integration, evaluation and responsible deployment, including how the product area's products and agents draw on the governed context layer with clear permissions and provenance.
• Resolve production AI failure modes as they arise.

AI-Native Experience

• Lead frontend architecture for the product area, defining the UI patterns and component library the team works from.
• Drive AI interface design that achieves measurable adoption, with confidence signals, error states, provenance and human-override paths, and guide others on UX engineering quality.

LLMOps & System Health

• Lead the team's LLMOps and observability strategy, designing evaluation pipelines and production monitoring at scale.
• Define standards for AI model lifecycle management, versioning and inference cost tracking across the product area.

Standards & Mentorship

• Set engineering standards for the team, written to be followed by humans and digital workers alike, with measurable improvements in test coverage, API contract quality and deployment reliability.
• Mentor junior and mid-level engineers through pairing, review and direct feedback, with visible improvement in their delivery capability.

Basic Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience
• 8+ years of combined experience across applied ML or data science and full-stack engineering, with significant time leading delivery of complex, user-facing AI products.
• Expertise across the AI product stack, including modelling and evaluation, modern frontend frameworks, backend services and cloud deployment, such as React or Next.js, Python or TypeScript, and AWS, Azure or GCP.
• Experience leading the design of API layers that serve multiple consumers, with integration contracts and non-functional requirements others build to.
• Experience designing AI orchestration at production scale: LLM integration, retrieval, tool and function calling, agent frameworks, evaluation, and cost, latency and quality trade-offs.
• Experience leading frontend architecture and LLMOps for a product area, including evaluation pipelines, production monitoring and model lifecycle management.
• Experience designing and operating agentic systems that drive continuous research or product iteration, and AI-assisted development patterns a team adopted.

Preferred Qualifications

• Master's or PhD preferred, in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
• Technical lead for one or more AI products combining modelling and full-stack delivery, from first build through to scale.
• Built or improved internal developer or research tooling, particularly for AI-assisted development and auto-research.
• Experience in a regulated industry, such as life sciences, where data integrity and auditability are mandatory.

Location and Working Model

• This role is based in the United States.
• Flexible working options available, including hybrid arrangements depending on team needs.