Cut LLM training costs by 40% with elite global engineering talent.
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Why Terminal for Reinforcement Learning?
Expert talent
- Rigorous vetting process that filters for top 7% of applicants
- Competent engineers that can provide high quality code examples and annotations, to generate more accurate, efficient, and readable code
Cost effective
- 40–60% savings compared to in-house teams or US-based contractors
- Flexible engagement models from project-based to dedicated teams
- Transparent pricing with no hidden fees
Flexible scalability
- Scale your RLHF teams up or down based on project requirements
- Quick onboarding within days, not months
- No long-term commitments or minimum engagements
How it works
1
Define project goals
2
Source team
3
Refining & labeling
4
We handle the rest
On-demand global talent
DevOps Engineer
5-10 Years Experience • Poland
Referred candidate
- 3 years of Tech Led experience
- M.S. Degree in Computer Science
- Worked for Facebook
Python Developer
5–10 Years Experience • Mexico
In Demand
0–1 Experience
- Skilled in multiple languages/frameworks
- 9 years of Tech Led experience
- Built 0 – 1 product
Fullstack Developer
5–10 Years Experience • Columbia
Referred candidate
- 3 years of Tech Led experience
- Worked for Mercado Libre
- Skilled in multiple languages/frameworks
Have a specific skill in mind for training?
Select from our extensive labor pool.
Go global. Cut costs. Train smarter.
Have questions?
We’ve got answers.
Where do you source your developers from?
We source talent from technology hubs across Latin America, Canada, and Europe. All engineers undergo a comprehensive vetting process to ensure they meet our high standards.
What use cases do you support?
We support a wide range of RLHF applications, including but not limited to text generation refinement, code completion models, conversational AI tuning, content moderation systems, and specialized domain adaptation. Our teams can work with both general-purpose and domain-specific models at various stages of development.