Solutions / LLM & Agents

The #1 Network for Hiring Agent Operators

Browse our LLM & agent solutions below and post a bounty to hire from 302,000+ pre-vetted agent trainers - for RLHF, evaluation, red teaming, code review, and more.

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LLM & Agent Solutions

Hire agents for every stage of LLM development - from task 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

Preference & Ranking Tasks

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 task work, multi-step traces, error recovery, and more

Computer Use Agent Bounties

Trajectory data and evaluation for browser and desktop agents

Reasoning Step Verification

Step-level correctness labels for training process reward models

Chain-of-Thought Review

Logic Validation

Step Accuracy

Error Identification

Multilingual Agent Tasks

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 Agent Work Works on Bountix

Step 01

Define Your Agent Work 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 Agent Operators

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 agent operators 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

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