Meridial

ML Engineer Specialist - Freelance AI Trainer Project

United States · Work style not stated

Posted over 6 months ago: check it’s still open

Apply on the employer’s site ↗See how well you match this job

You apply directly with the employer. ROLIVA does not submit applications from this page and is not the employer.

About the role

Description from the employer’s posting. Check the employer’s page for the current version.

EmployerMeridial
LocationUnited States
Work styleWork style not stated
Employer posted1 year ago

Do you enjoy Kaggle-style problem solving, iterating on models, testing ideas, and driving performance gains? Now imagine doing that as paid work, where your solutions directly strengthen the world’s most advanced AI systems.

Large-scale language models are evolving into engines of discovery, education, and engineering. To get there, they need sharper reasoning and better datasets. As a Machine Learning Specialist, you will take on hands-on ML challenges that feel like research competitions, but with direct impact on next-generation AI.

What You’ll Do
On a typical day, you will:

Solve applied ML tasks: iterating on problems that test and improve model reasoning, from baseline solutions to optimized workflows.
Evaluate scientific writing: compare research abstracts and introductions using review rubrics (e.g., NeurIPS guidelines) for clarity, correctness, and impact.
Debug advanced model behavior: probe LLMs on optimization, fairness, regularization, transformers, and applied NLP, documenting failure modes.
Design new benchmark tasks: identify datasets, analyze state-of-the-art papers, build evaluation scripts, and provide reference implementations.
Improve training datasets: deliver structured feedback that makes AI outputs more accurate, reliable, and useful.

Show full description

What you’ll Bring

Strong background in machine learning, NLP, or data science.
Experience with supervised/unsupervised learning, deep learning, reinforcement learning, probabilistic modeling, and applied NLP.
Proficiency in Python, ML frameworks, and cloud-based workflows.
Master’s or PhD in CS/ML/data science preferred; peer-reviewed research or production ML systems a strong plus.
Clear communication skills, able to explain reasoning and “show your work.”

Why Join

Turn your ML problem-solving into real-world impact: your work trains and strengthens cutting-edge AI.
Be part of novel AI training initiatives: evaluating models, shaping datasets, and designing challenges at the research frontier.
Enjoy flexibility: remote, contract-based work with autonomy to set your schedule.

Ready to turn your machine learning expertise into the knowledge base for tomorrow’s AI? Apply today and start teaching the model that will teach the world. We offer a pay range of $30-$50 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location.

Final offer amounts may vary from the pay range listed above. As a contractor you’ll supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply.

Job title: ML Engineer Specialist – AI Trainer
Employment type: Contract
Workplace type: Remote
Seniority level: Mid-Senior Level

See how well you match this job Apply on the employer’s site ↗

Check your resume against this job Free, no sign-up. Nothing you paste is stored.

Staying safe while you apply

Never pay to apply, interview or accept an offer. ROLIVA never asks you for payment to apply to a job.
Apply only through the official “Apply on the employer’s site” button, on the employer’s own site, and check that a recruiter’s email uses that employer’s own domain rather than a free email address.
Be cautious of an offer made with no real interview, or pressure to decide quickly or share financial details early.

Does something look wrong with this listing? Use “Report broken link” in the role details, or read ROLIVA’s full job-safety guidance ↗.

Preparing for this role with ROLIVA

Check the fit.
Compare the requirements with the experience you have confirmed, including gaps.
Prepare honestly.
Build interview stories and application drafts from your own evidence.
Keep track.
Save the role and record your own application status and follow-ups.

Create a free workspace or log in.