# Member of Technical Staff, Tech Lead Applied AI Backend at Mercor

- Company: Mercor
- What the company does: Mercor is organizing human intelligence to power the AI economy. We are powering frontier research, AI benchmarks, and AI agent training at scale for the top AI labs and enterprises. Backed by General Catalyst and Menlo.
- Company website: https://mercor.com/
- Type: Startups (AI role)
- Level: Senior
- Location: San Francisco
- Work setup: On-site
- Pay: $250K to $500K base salary per year (USD)
- Posted: 2026-09-24
- Apply by: 2026-11-08
- Apply: https://jobs.ashbyhq.com/mercor/76f19dab-be4a-4e1b-9971-3b3372666a05
- Page: https://www.1752.vc/careers/jobs/mercor-member-of-technical-staff-tech-lead-applied-ai-backend/

## About the role

The Applied AI org builds the systems that turn human expertise into training data for frontier models, task pipelines, expert workflows, evaluation infrastructure, and the services that tie them together. We have synthetic pipelines and modular quality control systems that run in unison to generate highest quality tasks and at scale. All of it runs on backend systems that have to stay reliable, fast, and observable while the volume behind them grows every month.

## What they're looking for

- 8+ years of professional backend engineering experience building and operating production systems with a track record of owning architecture across multiple teams and of decisions that aged well
- Experience mentoring senior engineers, not just junior ones
- Strong fundamentals in backend engineering: data structures, algorithms, concurrency, and writing code that's clear enough for the next person to change
- Hands-on experience with API design - REST, gRPC, or GraphQL, and an understanding of versioning, contracts, and backward compatibility
- Solid database skills : relational data modeling, indexing, query performance, transactions and isolation, and safe migrations. Familiarity with at least one NoSQL or key-value store and when it's the right choice
- Deep, hands-on expertise in distributed systems : queues and event streams, caching, idempotency, rate limiting, and designing for partial failure

Tags: Engineering