Software Engineer (AI-Native Platform & Integrations)

$70–110/hrrate as listed

Build and ship a multi-tenant AI-native SaaS platform end-to-end — integrations layer, real-time analytics, and an MCP-style agentic tool runtime — through pilot launch with real users. Hired via Mercor's talent network as a W-2 role administered by Cincinnatus, with observed rates of $70–110/hr.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work involves

This is product engineering, not annotation work. You own core delivery for a platform replacing spreadsheet-driven operations with live dashboards and agentic workflows: data models and permissions for a multi-tenant foundation, a connector framework for third-party tools (OAuth2 flows, webhook ingestion, paginated backfills, rate-limit handling, sync reconciliation), real-time analytics surfaces with drill-downs and exports, and an MCP-compatible tool layer covering registry, scoped permissions, runtime execution, and observability. You'll work across backend and frontend, set up testing and instrumentation, and be hands-on during the pilot — shipping, debugging, and iterating with users watching.

What the platform screens for

Mercor's AI interview probes for verifiable specifics rather than résumé keywords. Expect follow-ups on systems you actually built: how you handled duplicate webhook deliveries, what you did when a vendor API changed pagination semantics, how you scoped tool permissions so an agent couldn't take a destructive action. The listing's "vibe coding" language is about judgment — the screen looks for people who use AI coding tools aggressively but still hold a line on interfaces, tests, and guardrails, and who can explain a tradeoff clearly enough that a non-engineer stakeholder stays aligned.

Logistics

This is an open application to Mercor's talent network; passing the AI interview makes you eligible for matching as roles open, on a rolling basis.
Employment for this engagement is administered by Cincinnatus as employer of record — W-2, payroll, benefits, compliance. It is a structured role, not freelance or per-task project work.
Expect full-time or fixed-term commitment with close collaboration inside a client team, including on-call readiness around the pilot window.
Pay of $70–110/hr reflects rates observed on comparable listings; actual offers depend on scope, seniority, and client.

What they look for

Production full-stack engineering — shipping backend and frontend features end-to-end
Deep integrations experience: OAuth2, webhooks, API pagination, rate limiting, idempotent sync
SaaS architecture with multi-tenant data models, permissions, and API design
Familiarity with MCP-style tool interfaces or building tool servers for agent runtimes
Real-time analytics and dashboard implementation (live metrics, drill-downs, exports)
Engineering hygiene: automated testing, observability, on-call readiness

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Have you shipped a production SaaS feature end-to-end (backend and/or frontend)?
Production shipping experience · required
Have you built or maintained integrations with external APIs or third-party tools in production?
Integration work · required
Are you fluent in at least one modern backend language (Python, Go, Node.js, Rust, or similar) and at least one frontend framework (React, Vue, TypeScript, or similar)?
Programming language proficiency · required
Have you worked on a multi-tenant or role-based access control system?
SaaS architecture exposure · preferred

0 of 4 answered

Included with this listing

Interview prep — 12 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Whether the candidate has actually owned full-stack delivery in a real SaaS environment, not just contributed to one; ability to diagnose and learn from production failure.
A strong answer: Name a specific product, define your exact scope (backend, frontend, or both), describe the core data model decision, and explain a concrete production issue you hit and how you fixed it—not a generic 'performance problem' but something specific like 'auth token caching broke on multi-org switchover.'
Where people lose it: Talking about a feature or subsystem rather than a product; hand-waving the production failure ('we had to scale') instead of a concrete bug; claiming end-to-end ownership when they only owned one surface.