11 months ago
Base Salary
$130k - $500k/yr
Responsibilities
- Own agentic features end to end from scoping through implementation, launch, and iteration.
- Design and ship LLM agents, harnesses, verifiers, tools, prompts, and reliability policies.
- Build Python and FastAPI services plus Temporal and Modal pipelines for agent runs and human-in-the-loop review.
- Build reinforcement-learning environments, simulated enterprise applications, simulated coworkers, and data rooms for long-running agent tasks.
- Create tooling for statistical trajectory analysis and automated failure-mode detection.
- Develop Next.js and React interfaces, data models, structured agent context, and audit trails.
- Define agent quality standards, instrument traces, evaluate performance, and improve prompts, tools, guardrails, reliability, cost, latency, and UX.
- Partner with Product, Design, Research, and Operations to shape agent autonomy and approval workflows.
Requirements
- Strong software engineering ability to build scalable agentic products.
- Ability to design and ship LLM agents, harnesses, verifiers, tools, prompts, and policies.
- Ability to build backend services, orchestration pipelines, full-stack interfaces, and data infrastructure.
- Ability to define agent quality, instrument traces, analyze failure modes, and improve reliability, cost, latency, and user experience.
- Ability to collaborate with Product, Design, Research, and Operations partners.
Benefits
- Free Equinox membership
- Health, dental, and vision insurance
- In-person five days a week in San Francisco, NYC, or London offices
Tech Stack
Categories
About Mercor
We find the best experts in every professional domain and put their knowledge to work training frontier models. Through APEX, we measure whether those models can actually perform economically valuable work. We're also bringing that expertise to enterprises: deploying custom AI agents, staffing teams with vetted domain experts, and helping organizations encode their own knowledge into AI systems.