Python + Full-Stack (JS) Developer
Listing checked August 25, 2026 · pay as published by Turing
Overview
You will build and maintain code that trains and refines AI models in a remote contractor setup. Your day-to-day includes running model evaluations, creating datasets for Supervised Fine-Tuning (SFT), and helping researchers implement RLHF. You will also review peers' code and write clear rationales for model scoring. This role demands production-grade Python and JavaScript/TypeScript skills, plus mandatory Docker knowledge.
What You'll Do11
- 1Write and maintain clean, high-quality code that supports AI model training and tuning.
- 2Run model evaluations (Evals) to benchmark performance and analyze the results to guide improvements.
- 3Score and rank model responses to user prompts across many domains, checking them against defined criteria.
- 4Produce detailed explanations and rationales for each evaluation, showing strong reasoning and technical depth.
- 5Own Supervised Fine-Tuning (SFT) initiatives, including building and curating task-specific datasets.
- 6Work alongside researchers and annotators to implement RLHF and fine-tune reward models.
- 7Devise fresh evaluation strategies and workflows that keep the model aligned with user expectations and ethical standards.
- 8Craft and refine optimal answers to boost the model's clarity, relevance, and technical accuracy.
- 9Review colleagues' code and documentation, offering useful feedback and spotting potential improvements.
- 10Partner with cross-functional teams to boost model performance and help shape product updates.
- 11Investigate and adopt new tools and methods to strengthen AI training workflows.
Requirements9
- 1Bachelor's or Master's degree in engineering, computer science, or equivalent practical experience.
- 2Strong command of Python and JavaScript/TypeScript syntax and conventions.
- 3Proven track record of building web apps with modular code and scalable architectures, with emphasis on readability, security, and stability, including testing.
- 4Solid understanding of ES6 and at least one backend framework (Node.js or NestJS) plus one frontend framework (React, Angular, or Vue.js).
- 5Demonstrated ability to write readable, reusable, and maintainable code and to participate in code reviews.
- 6Excellent communication skills in English, both spoken and written.
- 7Mandatory hands-on experience with Docker.
- 8Bonus: prior experience with software quality assurance, test planning, or test-driven development.
- 9Bonus: experience working with large language models, including prompt engineering with ChatGPT or other LLMs.
Who Should Apply
The ideal candidate for this role brings strong production coding skills in Python and JavaScript/TypeScript, plus a habit of writing tests and participating in code reviews. They should be comfortable with Docker and have experience building web apps with a modern stack like Node.js or NestJS alongside React, Vue, or Angular. This role is less suitable for developers who prefer backend-only work or who dislike writing detailed written rationales for model evaluation. Applications fall short when candidates lack mandatory Docker experience, fail to demonstrate code quality through examples, or cannot commit to the required 4-hour PST overlap.
Pay and demand for AI Training roles
AggregatedTypical pay
$72/hour
This role
Pay not disclosed
Most AI Training roles pay $42–$98 per hour.
Based on 1,097 similar roles that publish pay · 302 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 1,272
- Listed in last 30 days
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- Remote
- 97%
Hiring most right now: micro1 (368) · SME Careers (253) · Mercor (162)
Most requested skills · share of roles
- ai training13%
- llm evaluation11%
- ai evaluation11%
- data annotation9%
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Compensation
Undisclosed by Turing
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Required Skills
Application Tip
In your application, include a code sample that demonstrates testing and code review practices in Python or TypeScript, and describe the Docker environment you used. If you have prior experience with LLM evaluations or prompt engineering, add a short note on how you completed those tasks.
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