ML Engineer Specialist - Freelance AI Trainer Project

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

$30-$50/hr From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

29% below the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

ML Engineer Specialist - Freelance AI Trainer Project United States of America 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. 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

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