Solutions / LLM & Agents

The #1 Network for Hiring AI Trainers

Browse our LLM & agent solutions below and post a job to hire from 124,000+ pre-vetted AI trainers — for RLHF, evaluation, red teaming, code review, and more.

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LLM & Agent Solutions

Hire domain experts for every stage of LLM development — from training data creation to evaluation, red teaming, and agent testing.

LLM Evaluation

Golden datasets, pairwise ranking, rubric scoring, judge validation, and more

Red Teaming

Jailbreaks, prompt injection, policy bypass, data leakage, and more

Adversarial Prompts

Jailbreak Testing

Bias Probing

Safety Boundary Testing

Hallucination Audits

Claim verification, citation checking, severity scoring, and more

RLHF & Preference Data

Pairwise ranking, multi-criteria scoring, rewrites, failure tagging, and more

Supervised Fine-Tuning

Instruction data creation, domain verification, synthetic data QA, and more

Code Generation Review

Verification, RLHF ranking, bug detection, security review, and more

Function Calling

Tool selection, argument annotation, multi-step traces, error recovery, and more

Computer Use Agent Training

Trajectory data and evaluation for browser and desktop agents

UI Navigation Tasks

Screenshot Annotation

Action Sequencing

Error Recovery Flows

Reasoning Step Verification

Step-level correctness labels for training process reward models

Chain-of-Thought Review

Logic Validation

Step Accuracy

Error Identification

Multilingual AI Training

Native-speaker RLHF, evaluation, prompt writing, and more across 100+ languages

Translation QA

Cross-Lingual Eval

Cultural Adaptation

Language-Specific SFT

Reasoning Problem Creation

Original problems with step-by-step solutions in math, code, and more

Problem Authoring

Multi-Step Puzzles

Domain Scenarios

Difficulty Calibration

Agent Simulations

Scenario design, gold trajectories, rollout evaluation, failure tagging, and more

Environment Design

Reward Shaping

Multi-Agent Scenarios

Trajectory Labeling

RL Environment Design

Reward functions, training scenarios, and environment QA for RL agents

State Space Design

Reward Functions

Environment Validation

Scenario Authoring

How It Works

How AI Training Works on OpenTrain

Step 01

Define Your AI Training Requirements

Describe the task type, domain expertise needed, evaluation framework, and quality bar. Whether it’s RLHF preference ranking, code review, red teaming, or multilingual evaluation — published pricing means no negotiation.

Step 02

We Screen & Match AI Trainers

Every candidate passes live domain assessments in their field — math PhDs for reasoning, senior devs for code review, linguists for multilingual tasks. You receive a curated shortlist with verified expertise.

Step 03

Review & Hire Your Team

Browse matched AI trainers with verified domain expertise, evaluation scores, and language capabilities. Pick exactly who fits your project requirements and quality standards.

Step 04

Deploy Into Your Training Pipeline

AI trainers work directly in your tools and frameworks — no platform migrations, no onboarding delays. They start contributing to your training pipeline on day one.