Software engineering experts
The hardest engineering problems rarely involve writing code. They involve understanding what happens when systems are under load, dependencies fail, and assumptions break down.
Current opportunities
Whether your background is backend engineering, infrastructure, or other you can get paid $80–$200/hr to help train frontier AI systems on real software engineering work.
Why Mercor
Work at the frontier of AI
Your expertise directly influences how future models reason about architecture, debugging, infrastructure, performance optimization, and production systems.
Earn top-tier compensation
Compensation ranges from $80–$200/hour based on expertise and specialization, with weekly payouts and opportunities for ongoing project work.
Flexible, remote work
Contribute from anywhere and choose projects that fit your schedule. Work independently without long-term commitments or fixed hours.
Apply deep technical expertise
Go beyond coding interviews and toy problems. Review AI reasoning across distributed systems, ML infrastructure, network operations, and other highly specialized domains.
The work
Each project is different, but engineering experts typically contribute in four ways:
Evaluate
Review and compare model outputs on realistic engineering tasks such as debugging production issues, tracing logic across repositories, implementing features, reviewing code quality, and assessing architectural decisions.
Create
Write expert-level engineering solutions, evaluation rubrics, reference implementations, and realistic technical scenarios that teach models how experienced engineers reason through complex problems.
Test
Assess how models handle ambiguity, incomplete requirements, architectural tradeoffs, conflicting constraints, and multi-step engineering workflows.
Review
Evaluate outputs for correctness, maintainability, security, production readiness, reasoning quality, and practical usefulness.
Expert perspectives
Lead Software & AI Engineer Expert
Meet Jared
Software Engineer
Product Manager, Lead Reviewer
How Mercor uses engineering expertise
We work with engineers who have spent years building, scaling, and maintaining complex systems. Their expertise helps evaluate and improve today's most advanced AI models—where correctness, performance, and reliability matter most.
Systems engineering
Help evaluate how AI approaches questions of scalability, reliability, concurrency, and system design. Assess whether solutions account for real-world operating conditions—or simply appear correct on paper.
Performance & hardware optimization
Determine how AI reasons about performance bottlenecks, parallel execution, memory efficiency, and hardware utilization. Identify what separates theoretically efficient solutions from systems that perform in practice.
Machine learning infrastructure
Review how AI approaches training systems, inference infrastructure, deployment workflows, and large-scale ML operations. Determine whether its solutions reflect the realities of running machine learning systems at scale.
Network engineering & operations
Analyze how AI reasons through packet traces, telemetry, and incident data. Identify whether conclusions are supported by the evidence or whether critical signals are being missed.
Enterprise & legacy systems
Examine how AI approaches modernization efforts, business-critical workflows, and legacy software platforms. Determine whether proposed solutions account for operational risk, system dependencies, and real-world constraints so systems don’t break.
Advanced programming languages
Assess how AI performs in specialized technical domains. Identify whether solutions demonstrate fluency of the programming language or simply resembles them on the surface.
Frequently Asked Questions
Strong candidates typically have professional software engineering experience and can independently reason through production systems and complex technical problems.
Experience with modern software development, backend systems, distributed systems, infrastructure tooling, large repositories, and production applications is commonly relevant across projects.
No. The work is substantially closer to software engineering, code review, debugging, architecture evaluation, and systems reasoning than traditional prompt engineering.
Strong candidates typically have:
- Professional experience building or maintaining production systems
- Ability to independently establish technical correctness
- Strong judgment around architecture, debugging, and engineering tradeoffs
- Experience distinguishing critical failures from non-material issues
- Ability to evaluate systems under uncertainty and incomplete requirements
- Clear written communication skills
- Ability to apply consistent evaluation standards across complex technical tasks
Compensation varies by project, specialization, and experience level.
Recent opportunities have included:
Software Engineering Expert
$80–$120/hour
Senior Software Engineering Expert
$100–$150/hour
Specialized Engineering Expert
$120–$200/hour
Engineering Review & Evaluation Projects
Starting at $50/hour