LLM Trainer Full-Stack Python + JS in Niuro

Remote (7 locations) | Senior | Full time | Programming

Gross salary $2500 - 3500 USD/month

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Niuro connects projects with elite tech teams, collaborating with leading U.S. companies. Our mission is to simplify global talent acquisition through innovative solutions that maximize efficiency and quality. Niuro empowers projects by providing autonomous, high-performance tech teams, specialized in partnering with top-tier U.S. companies. We offer continuous professional growth opportunities, training in technical and soft skills, and leadership development to ensure success in innovative and challenging projects. Additionally, we handle all administrative tasks, including hiring, payments, and training, allowing our teams to focus on delivering exceptional results. Our priority is to foster a collaborative environment where talent can grow and reach its full potential.

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Role responsibilities

Write, review, and debug code across multiple languages
Design tasks and evaluation scenarios for coding, reasoning, and debugging
Investigate LLM outputs and identify hallucinations, regressions, and failure modes
Build reproducible dev environments using Docker + automation tools
Develop scripts, pipelines, and tools for data generation, scoring, and validation
Produce structured annotations, judgments, and high-quality datasets
Run systematic evaluations that help improve model reliability and reasoning

Required skills & experience

We’re seeking a seasoned coder-scientist to join as an LLM Trainer on a Frontier AI Lab partnership. You will deliver production-quality code, design rigorous evaluation scenarios, and create reproducible workflows to produce clean, high-signal data for model training. Precision and thoroughness are critical—small mistakes can cascade into misleading results. You will collaborate with engineers, researchers, and quality owners to align on standards, review work, and elevate the overall quality bar. If you enjoy solving unusual technical problems, diagnosing subtle model failures, and working in a developer-like environment where correctness and collaboration matter, you’ll thrive here.

Hands-on coding experience in Python, JavaScript/Node.js, TypeScript (professional or research-backed)
Linux, Bash, scripting, and automation
Docker, reproducible environments, and dev containers
Git proficiency (branching, diffs, patches, conflict resolution)
Testing and QA understanding (unit, integration, edge cases)
Willingness to overlap with 8am–12pm PT for collaboration

Nice-to-haves

Nice-to-have experience with LLM coding tools (Cursor, Copilot, CodeWhisperer); dataset creation, annotation, evaluation, or ML pipelines; familiarity with benchmarks like SWE Bench or Terminal Bench; background in QA automation, DevOps, ML systems, or data engineering; advanced degree in a technical field aligned with required years of experience.

What we offer

We provide the opportunity to participate in impactful and technically rigorous industrial data projects that drive innovation and professional growth. Our work environment emphasizes technical excellence, collaboration, and continuous innovation.

Niuro supports a 100% remote work model, allowing flexibility in work location globally. We invest in career development through ongoing training programs and leadership opportunities, ensuring continuous growth and success.

Upon successful completion of the initial contract, there is potential for long-term collaboration and stable, full-time employment, reflecting our long-term commitment to our team members.

Joining Niuro means becoming part of a global community dedicated to technological excellence and benefiting from a strong administrative support infrastructure that enables you to focus on impactful work without distraction.

GETONBRD Job ID: 58352

Informal dress code No dress code is enforced.

Remote work policy

Locally remote only

Position is 100% remote, but candidates must reside in South America, Nicaragua, Guatemala, Dominican Republic, Mexico, Costa Rica or Honduras.

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