Backend Engineer Talent Network

What you would do

• Create realistic backend engineering problems based on scenarios you've encountered
• Evaluate AI-generated API designs, database schemas, and system architectures
• Assess whether AI understands trade-offs between consistency, availability, scalability, and complexity
• Grade code and design decisions for production readiness
• Provide detailed feedback on microservices, distributed systems, and optimization

Who they want

• Professional backend engineering experience with API development and microservices
• Demonstrated expertise in database design, optimization, and distributed systems
• Strong communication skills for explaining technical decisions and feedback
• Comfortable working solo in remote settings without constant oversight
• Willingness to work on flexible schedules ranging from 15-30 hours per week

Main skills

What the interview asks about

1. 1.API and microservice design

   Backend systems live or die on their API contracts and service boundaries; AI must learn design patterns that scale without constant refactoring.

   For example: “You're designing an order-processing system as a set of microservices. How would you decompose it, and what are the key trade-offs between having one large order service vs. separate inventory, payment, and fulfillment services?”

2. 2.Database architecture choices

   AI needs to understand why certain data models, indexing strategies, and consistency models work for specific problems.

   For example: “An AI proposes using a single relational database for all microservices to maintain strict ACID guarantees. What are the architectural consequences, and when would this trade-off be wrong?”

3. 3.Distributed systems reasoning

   At scale, systems must tolerate failure and manage consistency; AI often ignores these hard realities.

   For example: “You're implementing a distributed cache that sits between services. An AI suggests a simple in-memory hash with no replication. What failures would this design not survive, and what would you recommend instead?”

4. 4.Performance under load

   AI must learn how systems degrade and what bottlenecks appear as traffic grows from 100 requests/second to 100,000.

   For example: “A backend service is provisioned to handle 10,000 requests per second. The AI proposes a design with N+1 database queries per request. Walk me through how you'd assess scalability.”

5. 5.Engineering trade-offs in practice

   Perfect consistency, zero latency, and infinite scale are impossible; AI needs to learn realistic prioritization.

   For example: “You're building an e-commerce cart service. The AI suggests strong consistency for cart state but eventually-consistent inventory. What are the real-world consequences of this choice, and how would you score it?”

A task you may get

Design a backend system for a scenario you've worked on or a domain you know well. Outline the services, database schema, and communication patterns. Then evaluate a sample AI-generated backend design and identify its strengths and weaknesses.

How to prepare

• Identify a backend system you've built or maintained and be ready to discuss its architecture and key decisions
• Prepare an example where a database choice or API design had to be changed due to scaling or operational issues
• Think about the difference between a theoretically sound design and one that works reliably in production

The facts

Pay

$70–150/hr

Hours

Part time

Where

Remote

Field

Software Engineering

Role type

Talent network

Posted

2/20/2026

We wrote this page from the public Mercor listing. It may be out of date, so read the full posting before you apply.