In 1600, William Gilbert published De Magnete —the first systematic study of magnetism. He didn't just theorize; he built instruments, ran experiments, and shared what he learned so that others could go further. Three centuries later, those foundations helped power the modern world
At Mercury, we're making a deliberate, company-wide bet on AI. Frontier users are already pushing boundaries—building agents, automating workflows, moving fast. But they're doing it in silos. This role exists to change that: to take those scattered experiments and turn them into shared infrastructure, shared context, and shared capability. The goal is a multiplier effect—where the most ambitious AI work inside Mercury lifts the velocity of everyone else
What you'll do You'll join a team that has already started building Mercury's internal AI platform and enablement layer. Your work will be to extend, harden, and scale what's in motion , and to help partner teams adopt it
Extend the AI platform foundation Build and evolve MCP servers that connect internal systems and data sources into a coherent interface for agents and engineers
Expand and operate our LLM gateway infrastructure: routing, rate limiting, cost attribution, and observability across teams
Turn early patterns into durable defaults: shared prompt libraries, guardrails, and policy-as-code so teams can move fast safely
Strengthen the shared company knowledge layer Shape and maintain structured context artifacts—clean, reliable, agent-consumable—so LLMs working in Mercury's systems can reason accurately about our domain
Improve internal knowledge discoverability and retrieval so both humans and agents can quickly find accurate answers
Partner with domain teams to standardize key sources of truth, and keep them fresh
Enable faster prototyping and iteration across the company Build and refine sandbox environments and tooling that let engineers experiment with AI safely and at speed
Create self-service scaffolding so non-engineers—PMs, ops, finance—can prototype and deploy AI-powered workflows with minimal hand-holding
Build playgrounds and evaluation harnesses so internal AI agents can be tested and iterated in controlled environments before hitting production
This list is illustrative. Priorities will shift as we learn; the right person will help choose the next highest-leverage work
The ideal candidate Has 5+ years of backend development experience in complex, production systems—you've built things that other engineers depended on
Is fluent across programming languages and can navigate platform engineering, infrastructure, and developer tooling without needing a map