2 months ago
Base Salary
$130k - $500k/yr
Responsibilities
- Design, build, and own backend services for agentic workflows, including orchestration, tool calling, retries, fallbacks, and human-in-the-loop review.
- Ship production agents and iterate on prompts, tools, guardrails, cost, and latency based on real usage.
- Build offline and online agent evaluation infrastructure with regression detection and operational metrics.
- Design scalable core services, data models, and APIs for Mercor’s talent network.
- Make agent decisions observable, auditable, and reversible.
- Partner with product, operations, and AI researchers to determine where agents, deterministic systems, and human review belong.
- Establish backend engineering standards and raise the technical bar of other engineers.
Requirements
- 5+ years of professional backend engineering experience.
- Strong fundamentals in distributed systems, service architecture, data modeling, and API design at scale.
- Production experience building with LLMs, agents, tool use, retrieval, or evaluation systems, or a demonstrated ability to learn new systems quickly and ship them.
- Experience owning systems end to end from design through production operation.
- Fluency with modern AI development tools such as Cursor, Claude Code, GitHub Copilot, or similar tools.
- Strong technical judgment, clear writing, and the ability to communicate complex tradeoffs to engineers and leadership.
- High ownership, pragmatism, comfort with ambiguity, and a bias toward shipping.
Benefits
- Generous equity grant vested over four years.
- Relocation and housing bonuses may be available.
- Monthly meal stipend.
- Free Equinox membership.
- Health insurance.
- In-person work five days per week in the San Francisco office; SF is the stated location preference.
Categories
About Mercor
Mercor builds an expert-powered platform that trains, evaluates, and deploys AI systems for AI labs and enterprises. It operates APEX to assess model performance on economically valuable tasks, and provides services such as custom AI agents and teams of vetted domain experts who encode organizational knowledge into AI. Founded in 2023 and headquartered in San Francisco, it is privately held and works with frontier AI labs and enterprise clients requiring strict data isolation.