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Mercor is building the infrastructure layer between human expertise and frontier AI models. The company operates a platform where millions of domain experts train AI models, and is now bringing this infrastructure to enterprise customers through Mercor Enterprise—helping Fortune 500 companies capture how their best people work and translate that expertise into AI agents.
You will join as a Member of Technical Staff on the Enterprise Platform team, responsible for designing, building, and operating the core infrastructure that powers agent deployment and execution. This is a full-stack platform and infrastructure engineering role working across security, routing, execution, and intelligence planes.
Key responsibilities include:
- Design and build foundational services: authentication/authorization, credential brokering, routing, sandboxing, and orchestration
- Define interfaces for Applied AI engineers building agents, ensuring the best practices are the easiest path
- Build event-driven systems that maintain idempotency at scale
- Own the deployment model across hosted and customer VPC environments
- Ensure tenant isolation by design, preventing cross-contamination and quota violations
- Optimize latency, throughput, and cost per agent run as volume grows
- Set architectural direction for platform components you own
- Partner with Applied AI engineers on production challenges from customer deployments
You'll work with Go, TypeScript, Postgres, Kubernetes, Harbor, and Terraform. The platform currently scales to thousands of agent runs per day and is growing rapidly. This is one of the few places where enterprise agent platform architecture is being answered in production with economically viable agents as the success metric.
The role requires 5+ years building high-scale, complex software systems, with demonstrated experience building and operating platforms that other engineers depend on. You should be hands-on with infrastructure primitives like authentication, secrets management, isolation, orchestration, and durable state across multiple clouds. A drive to continuously optimize for speed and cost, ability to work independently on ambiguous problems, and bonus experience with LLM applications or agent frameworks are valued.
