[Hiring] Senior Software Engineer - AI Enablement @Mercury

Role Description

At Mercury, we're making a deliberate, company-wide bet on AI. This role exists to take 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.

You'll join a team that has already started building Mercury's internal AI platform and enablement layer. Your work will be to:

• 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.

Qualifications

• 5+ years of backend development experience in complex, production systems.
• Fluent across programming languages and can navigate platform engineering, infrastructure, and developer tooling.
• Hands-on experience building LLM-powered systems—RAG pipelines, agents, eval frameworks.
• Understanding of the real tradeoffs in AI deployments: cost modeling, observability, latency, and safety.
• High-agency and self-directed; can operate effectively without tightly-defined scope.
• Clear communication across technical and non-technical audiences.

Requirements

• Experience in backend development.
• Proficiency in multiple programming languages.
• Experience with LLM-powered systems.
• Knowledge of AI deployment tradeoffs.
• Ability to work independently and prioritize effectively.
• Strong communication skills.

Benefits

• Base salary and equity.
• Highly competitive salary and equity ranges updated regularly.
• New hire offers based on experience, expertise, geographic location, and internal pay equity.
• Target new hire base salary ranges for this role:
  • US employees (any location): $166,600 - $218,700
  • Canadian employees (any location): CAD 157,400 - 206,650