Senior Software Engineer - AI Engineering

Benefits

Parental leave

Not verified not verified - source not recorded; timestamp not recorded

Non-birth-parent leave

Not verified not verified - source not recorded; timestamp not recorded

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Not verified

Salary

Not verified not verified - source not recorded; timestamp not recorded

Was this benefit information wrong? Tell us.

Schedule

Shift type

Not verified

Weekend work

Not verified

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Senior Software Engineer - AI Engineering San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States 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

Read the full description at job-boards.greenhouse.io. FewerJobs shows a source-linked preview and links to the original posting.