What Is Handshake AI? Expert Network for AI Model Training
Handshake is a career platform for students and recent graduates. According to various contributor reports, some users access AI evaluation work through the platform. The platform facilitates connections between contributors and AI training projects through a credential-verification system. Contributors complete evaluation tasks, ranking responses and writing justifications for AI outputs used in reinforcement learning from human feedback (RLHF) training workflows.
Key takeaways
- Handshake restricts access to university-affiliated talent with verified academic credentials, distinguishing it from generalist crowd platforms like Outlier (Scale AI) and DataAnnotation.
- Contributors complete evaluation tasks, ranking responses and writing justifications for AI outputs used in RLHF training workflows.
- Eligibility requires current student, recent graduate, or PhD status with US work authorization; F-1 visa holders with CPT or OPT approval may qualify.
- Work types include response ranking, prompt engineering, model output evaluation, fact-checking, and justification writing across generalist and specialized domains.
- The AI Evaluator Certification from Annotation Academy covers justification writing, rubric application, and response quality assessment, core skills required for AI evaluation project success.
Understanding credential-gated expert networks matters for anyone pursuing AI evaluation work. Those serious about evaluation platforms should also explore the distinction between AI evaluation and data annotation, and understand how credential-gated platforms differ from open-access ones. Evaluators planning advanced roles should consider the AI Evaluator Certification from Annotation Academy, which covers skills transferable across major evaluation platforms.
What does Handshake mean?
Handshake is a credential-gated career network that matches verified students, graduates, and PhDs with asynchronous projects requiring domain expertise. The platform operates as a college recruitment tool connecting academic talent with various opportunities. Contributors can complete evaluation tasks, write justifications for AI responses, and create training data used in RLHF workflows. Unlike generalist annotation platforms, credential-gated networks restrict access to university-affiliated talent with verified academic credentials.
The credential gate exists because frontier AI labs, including OpenAI, Anthropic, and other major companies, contract specialized evaluation work requiring advanced degrees and domain knowledge. Projects spanning medical research, legal analysis, and STEM fields demand evaluators who can identify technical errors and assess specialized claim validity. This structure contrasts with platforms like Mercor and Micro1, which use similar expert network models but may have different credential thresholds.
How do credential-gated evaluation networks work?
Credential-gated networks typically operate through a project-based assignment system where contributors apply to specific projects matching their academic background. Work is asynchronous, with evaluators completing tasks on their own schedule. Payment processing varies by platform and contributor location. Expert networks have expanded significantly to serve contributors across hundreds of active projects.
Who can join credential-gated evaluation networks?
Eligibility typically requires current student, recent graduate, or PhD status with valid US work authorization. F-1 visa holders with Curricular Practical Training (CPT) or Optional Practical Training (OPT) approval generally qualify according to platform documentation. STEM OPT extensions may have varying support depending on the specific platform.
Applicants must verify their academic status through platform networks. The verification process confirms enrollment or degree completion before project access is granted. This credential check is the primary difference between credential-gated networks and open-access platforms like Outlier (Scale AI) and DataAnnotation, which accept applicants meeting basic qualification tests.
What types of work are available?
Work includes response ranking, prompt writing, model output evaluation, fact-checking, and justification authoring. Projects span generalist tasks and specialized domains. Availability fluctuates based on AI lab demand and project cycles. Many projects require passing initial skill assessments or completing sample tasks before gaining full access to higher-paying work.
Response ranking involves comparing two or more LLM outputs and selecting the higher-quality response with written justification. Prompt writing tasks ask contributors to design effective inputs for language models. Model output evaluation assesses whether responses meet accuracy, safety, and factual correctness standards. Justification authoring, writing detailed explanations for quality judgments, is a core skill across all project types and is covered extensively in the AI Evaluator Certification from Annotation Academy.
What is a real example of AI evaluation work?
A biology PhD working on a model evaluation project receives a prompt asking an LLM to explain Crispr gene editing mechanisms. The evaluator reviews two model-generated responses, ranks them by accuracy and clarity, and writes a 150-word justification citing specific technical errors in the lower-ranked response. Unsupported claims about clinical applications are flagged.
The task takes approximately 12 minutes to complete. The evaluator submits the evaluation through the platform web interface using a structured form. This workflow demonstrates how domain expertise converts into training signal. The justification text becomes part of the dataset teaching the model to differentiate high-quality biology explanations from flawed ones. This same justification-writing process is central to how RLHF training works across all major evaluation platforms.
How do credential-gated networks compare to other evaluation platforms?
Credential-gated networks occupy the expertise-intensive end of the evaluation market. Platforms like Mercor and Micro1 use similar expert network models targeting specialized contributors with advanced degrees. Scale AI's Outlier platform and DataAnnotation accept generalist applicants with lower barrier requirements, trading credential verification for broader contributor pools. Understanding these differences helps evaluators choose platforms matching their background and income goals.
Expert network vs. crowd model
Expert networks screen for verified credentials and academic affiliation. Crowd platforms (Outlier, DataAnnotation, Mindrift) accept applicants meeting basic qualification tests. The credential gate creates higher pay floors but restricts project volume. Generalist contributors access more consistent work through crowd platforms, while PhD-level specialists find higher rates on expert networks. This trade-off shapes platform choice for most evaluators.
The expert network model prioritizes depth and specialization. Projects require strong domain knowledge and often test analytical writing ability. Crowd platforms prioritize volume and accessibility, favoring speed and consistency over specialized expertise. Mercor, another expert network, uses similar credential filtering but connects to a different set of AI labs and enterprise clients.
Pay and credential requirements
Average hourly pay on credential-gated networks reflects credential requirements and project complexity. Verified official listings show ranges scaling with education level and project specialization. Frontier AI labs contract through such networks for domain-specific evaluation work requiring advanced degrees. Contributors with PhDs in STEM fields typically access higher-paying projects than recent graduates.
Crowd platforms like Outlier (Scale AI) and DataAnnotation offer more consistent work volume but generally lower per-task rates. Expert networks trade consistency for higher individual task pay. The choice depends on whether a contributor prioritizes stable monthly income or maximum hourly rates on specialized work.
What payment and certification information should you know?
Payment processing methods and timelines vary by platform and contributor location. Contributors should document completed tasks using time-tracking tools and maintain independent records of completed work outside the platform interface.
The AI Evaluator Certification from Annotation Academy prepares evaluators for technical requirements common across expert networks. The certification comprises 24 modules covering 30+ hours of instruction and 800+ practice questions. Core topics include justification writing, rubric application, response quality assessment, RLHF fundamentals, prompt engineering, and safety evaluation, all skills tested during project applications. Completing the certification before applying strengthens project qualification rates and accelerates onboarding.
Evaluators working across multiple platforms benefit from understanding the AI evaluator vs. data annotator distinction. Expert networks emphasize evaluation over raw annotation, requiring stronger analytical writing and domain expertise. Those serious about building sustainable income across multiple platforms should explore the full AI evaluation career outlook before committing exclusively to any single network. The AI Evaluator Certification from Annotation Academy is recognized across major platforms and provides portable, platform-agnostic skills.
Related terms
AI Evaluation Platforms: Marketplaces connecting human evaluators with AI training projects across different credential and pay tiers, including expert networks and crowd platforms.
Expert Network: Credential-verified talent pools matching specialists with projects requiring domain expertise beyond general annotation skills.
RLHF (Reinforcement Learning from Human Feedback): The training methodology underlying most AI evaluation work, where human evaluators provide preference signals teaching models to generate better outputs.
Model Output Evaluation: The core task type on expert networks, requiring evaluators to assess LLM responses against accuracy, safety, and quality criteria.
Prompt Engineering: The skill of designing effective inputs for language models, often combined with evaluation work on platforms.
CPT and OPT: Curricular Practical Training and Optional Practical Training, visa authorization categories allowing eligible F-1 international students to work on credential-gated evaluation networks.
Ready to build AI evaluation skills? The AI Evaluator Certification at Annotation Academy, 24 modules, 30+ hours, 800+ practice questions, prepares you for work on Mercor, Scale AI's Outlier, and other major evaluation platforms. One-time payment of $249, lifetime access. Enroll today at annotation.academy.
