AI Platform Engineer

Who we are

At Frontiers, our purpose is simple yet ambitious: to make science open. We believe open science empowers the global scientific community to accelerate discovery and develop the solutions needed for healthy lives on a healthy planet.

We are one of the world’s largest and most influential open-access research publishers. Every article we publish is peer-reviewed and quality-certified, ensuring research is accessible to everyone, everywhere. To date, Frontiers research has been viewed over 4 billion times, demonstrating the real-world impact of science without barriers

Joining Frontiers means being part of a global, mission-driven organisation at the intersection of science, technology, and innovation — working alongside passionate colleagues who care deeply about advancing knowledge for the benefit of society.

To learn more about our impact and culture, please watch this video:

FrontiersOS puts AI agents inside the systems Frontiers already runs — the Publishing Platform, the data warehouse, Salesforce, Microsoft 365 — and replaces none of them. The bet: AI should multiply what each person can do, not just add another tool to their day. Work already happens everywhere — an inbox, a review forum, a CRM tab, a warehouse query — so the agents live everywhere too, taking the repetitive half and leaving people the half only they can do: strategy, new markets, the relationships that move science.

The Innovation team designs, engineers, and runs FrontiersOS end to end, wired directly into real business problems across editorial, research integrity, and publishing operations. We're hiring a senior engineer with serious engineering depth and strong AI fluency — someone who can design and build the systems that make agentic work possible at Frontiers, not just consume an LLM API and call it done.

This is a full-stack platform role on a small team: you won't be handed a narrow backend slice and someone else's frontend — you'll move across the whole system as the problem demands.

Multi-provider LLM routing, an agent tool-calling framework, retrieval-augmented generation and vector search, LLM evaluation and tracing, durable workflow orchestration, message queues, caching, epistemic cloud sandboxes, container orchestration, a relational database, object storage, identity, and a cloud data warehouse — the real stack FrontiersOS runs on today, not a hypothetical one.

• Design, build, and operate FrontiersOS end to end — architecture, backend, frontend, model orchestration, evals, and the access surfaces (browser extension, native clients, embeddable widget, public API) people actually use it through, not just the agent/skill core.
• Build for self-service, not for engineers: editors, analysts, and marketers assemble their own agents, skills, automations, and apps with no engineering queue to wait in — your job is the paved paths, guardrails, and product judgment that make that safe and usable for people who don't write code.
• Architect for reliability and scale: this platform underpins critical production workflows across the company.
• Build the auditing and evaluation layer for agents and other agentic systems.
• Use AI-assisted development as a default part of your own workflow, while knowing precisely when generated code needs deep review before it ships.
• Take full accountability for what you ship, including work substantially generated by agents you built or are using.

Requirements

• 6+ years of engineering experience, including 1-2 years of proven production AI/LLM delivery.
• Strong engineering fundamentals: solid system design and architecture skills, writing code that's simple and well-structured, and sound software design principles. You're the one accountable for reviewing and standing behind what gets shipped.
• Systems thinking: you reason about how a change ripples across agents, workflows, permissions, and downstream consumers of the platform, not just the module you're editing.
• Strong, demonstrated AI fluency: hands‑on building with LLM APIs, agent frameworks, and AI coding tools — not familiarity, usage.
• Experience with evaluation methodology for…