Coders: Smarter than AI? Prove It
The models shaping the future of AI are only as rigorous as the coders behind them. Apply your expertise where it matters, and get paid to do it.
Kevin
As seen on
This is not busywork.
The most advanced code generation models still produce race conditions, ignore edge cases, hallucinate APIs, and write O(n³) solutions when linear time is trivial. You're the engineering filter that catches what compilers can't — one annotation at a time.
Three steps.
No overhead.
Apply & Qualify
Complete a short engineering assessment. We evaluate your ability to identify correctness bugs, reason about complexity, and write clean, idiomatic code. No résumé required.
Get Matched
Based on your preferred stack and assessment results, you're matched to projects in your areas of expertise. Systems programming, web backends, ML pipelines, mobile, infra — you choose what fits your background.
Work & Get Paid
Complete tasks on your own schedule. Each task has clear specifications, test cases, and a defined scope. Payment is per-task, processed weekly, starting at $50-$75+/hour
What you'll actually do.
Write a prompt
Craft precise engineering prompts designed to probe the boundaries of model reasoning — not toy problems, but challenges that expose incorrect algorithms, unsafe memory access, and flawed system design decisions.
Review AI output
Evaluate whether the model's code actually works — catching off-by-one errors in loop logic, identifying race conditions in concurrent implementations, and flagging O(n²) solutions where a more efficient algorithm was straightforward.
Write the correct solution
Author a complete, production-quality implementation that demonstrates the correct approach. This becomes training signal for the next model generation.
Where models need you most.
These are the engineering domains where current AI code generation consistently produces dangerous output. Your expertise directly addresses the hardest open problems in coding AI.
Systems & Infrastructure
Evaluate AI reasoning about distributed systems, networking, operating systems, and infrastructure design. Verify claims about consensus protocols, storage engines, and scalability trade-offs.
Algorithms & Data Structures
Review model outputs on algorithm design, complexity analysis, and data structure selection. Catch incorrect Big-O claims, flawed dynamic programming formulations, and suboptimal data structure choices.
Security & Cryptography
Assess AI-generated code for security vulnerabilities, cryptographic misuse, and authentication flaws. Identify injection vectors, broken crypto implementations, and authorization bypass patterns.
Backend & API Design
Evaluate reasoning about API architecture, database design, caching strategies, and service communication. Identify N+1 queries, incorrect transaction isolation levels, and flawed idempotency implementations.
Frontend & Performance
Review model outputs on frontend architecture, rendering performance, state management, and accessibility. Verify claims about bundle optimization, hydration strategies, and browser API usage.
A selective community of experts
Work alongside writers, lawyers, health professionals, coders, professors, and bilingual experts shaping the future of AI.
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The problems here actually require engineering judgment. It's some of the most rigorous work I've done outside of a staff role.
Marcus
Software Engineer
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The variety is what I didn't expect. One week I'm rewriting technical explanations, the next I'm evaluating how a model handles tone and voice.
Julia
Senior Copywriter
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Clinical nuance matters in this work. It's rare to find contract work where my specialty actually gets used properly.
Melissa
Clinical Reviewer
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The evaluation work here is more rigorous than most PM research I've run in-house. I've genuinely changed how I think about product quality.
Azhar
Senior Product Manager
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Legal reasoning in AI is still an unsolved problem. Getting to shape how these models handle it, from a practitioner's perspective, is the work I actually want to be doing.
Krysta
Solicitor
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The code review standards here are higher than most teams I've worked on. It's sharpened how I think about edge cases in my day job.
Tyler
Senior Engineer
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Pressure-testing these tools surfaces failure modes that matter. The work holds up to the standards I'd apply in practice.
Abi
Litigation Attorney
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Most freelance work in my field is content mills. This is the first engagement where my clinical training is the reason I was hired, not an afterthought.
Liana
Clinical Researcher
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My writing background is exactly what this work needs. Evaluating how models handle narrative, voice, and subtext is surprisingly rigorous.
Mi Ji
Screenwriter
Paid to contributors
Expert annotators
Annotations completed
Built for people who think in systems.
CS Students
Upper-division and graduate CS students with strong foundations in algorithms, systems, and software engineering principles.
Software Engineers
Professional developers with production experience looking for flexible, intellectually demanding work outside their day jobs.
Staff & Principal Eng.
Senior engineers applying deep systems knowledge and architectural expertise to AI training data on their own schedule.
Open-Source Contributors
Maintainers and active contributors with demonstrated ability to write clean, well-tested, production-quality code.
Frequently Asked Questions
Have questions? Here we answer the most common questions.
You'll work on tasks that help AI perform better, like reviewing responses, checking accuracy, refining prompts, and rating outputs. No coding or technical background needed.
Projects vary but commonly include reviewing AI-generated text, ranking responses, checking facts, labeling data, and reviewing content in specific subject areas. All focused on improving real production AI systems.
This is ideal for people with strong reasoning skills, attention to detail, and subject-matter expertise who want flexible, remote work with real impact. Researchers, students, professionals, and independent contributors who enjoy analytical work and want to contribute to advancing AI systems.
This is a flexible, task-based contractor role. You work remotely on your own schedule, focused on output quality rather than hours logged. There is no long-term commitment required, and you can work as much or as little as you choose.
Get ahead in a changing workforce.
No recruiters. No interviews. Just meaningful work and real compensation.