--- title: "AI Trainer (ML Engineering) at micro1 – All Job Board" --- # AI Trainer (ML Engineering) at micro1 Written by [admin](https://alljobboard.com/author/admin/) in - [Remote (Remote)](https://maps.google.com/maps?q=Remote%20%28Remote%29&zoom=14&size=512x512&maptype=roadmap&sensor=false) - Posted 5 days ago ![micro1](https://alljobboard.com/wp-content/plugins/wp-job-manager/assets/images/company.png) **micro1** **Pay:** $60–$120/hour equivalent, paid per accepted task **Location:** Global, fully remote **Job Type:** Contractor **Time Commitment:** 15+ hours per week **Schedule:** Flexible micro1 is looking for Machine Learning Engineers with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems. You will use your machine learning expertise to create challenging technical tasks, evaluate model-generated work, identify failures, and establish objective standards for correct solutions. This role is intended for candidates who have already completed substantive AI-training work. ### What You’ll Work On 1. Create realistic machine learning engineering tasks that challenge advanced AI models. 2. Develop task instructions, reproducible environments, reference solutions, and evaluation criteria. 3. Evaluate model-generated code, experiments, analyses, and technical deliverables. 4. Identify issues involving data leakage, incorrect validation, unstable training, performance, or reproducibility. ### Required Qualifications 1. Prior paid experience training, evaluating, benchmarking, or improving AI systems. 2. Strong practical proficiency with Python. 3. Hands-on experience using PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, LightGBM, or comparable ML tools. ### Public Proof of Skill Strong applicants should provide evidence such as: 1. GitHub repositories or public model implementations 2. Kaggle, DrivenData, AIcrowd, or similar competition results 3. Hugging Face models or datasets 4. Research publications or technical reports 5. Open-source ML contributions 6. Public benchmarks, experiments, or technical projects ### Preferred Qualifications 1. Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems. 2. Experience reviewing other AI trainers’ technical submissions. 3. Kaggle Expert, Master, or Grandmaster status, or strong results in another technical competition. 4. Publications or open-source contributions in machine learning. 5. Prior experience with long-horizon or agentic AI evaluations. 6. Availability to begin immediately. ### Application Process 1. Submit your application, AI-training experience, and public technical evidence. 2. Complete an AI interview containing technical and integrity checks. 3. Complete a hiring-manager review. 4. If selected, complete project onboarding and begin your first assigned task. ### Compensation 1. Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary. Originally posted on Himalayas **Source:** this listing was published by [Himalayas](https://himalayas.app/). [View the original posting and apply](https://himalayas.app/companies/micro1/jobs/ai-trainer-ml-engineering?utm_source=api) — applications are handled there, not on this site. To apply for this job please visit [himalayas.app](https://himalayas.app/companies/micro1/jobs/ai-trainer-ml-engineering?utm_source=api). ## More posts - ### [Holiday Party Networking: How to Job Hunt Without Making It Awkward](https://alljobboard.com/holiday-party-networking-job-search/) [November 13, 2026](https://alljobboard.com/holiday-party-networking-job-search/) - ### [How to Negotiate a Start Date After the Holidays (With Scripts)](https://alljobboard.com/negotiate-start-date-after-holidays/) [November 13, 2026](https://alljobboard.com/negotiate-start-date-after-holidays/) - ### [Should You Quit Before or After Your Year-End Bonus?](https://alljobboard.com/quit-before-or-after-year-end-bonus/) [November 13, 2026](https://alljobboard.com/quit-before-or-after-year-end-bonus/) - ### [How to List a Seasonal Job on Your Resume (With Examples)](https://alljobboard.com/seasonal-job-on-resume/) [November 13, 2026](https://alljobboard.com/seasonal-job-on-resume/) ```json { "@context": "http://schema.org/", "@type": "JobPosting", "datePosted": "2026-11-09T18:19:40+00:00", "validThrough": "2027-01-08T23:59:59+00:00", "title": "AI Trainer (ML Engineering) at micro1", "description": "

Pay: $60–$120/hour equivalent, paid per accepted task

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Location: Global, fully remote

\n

Job Type: Contractor

\n

Time Commitment: 15+ hours per week

\n

Schedule: Flexible

\n

micro1 is looking for Machine Learning Engineers with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems.

\n

You will use your machine learning expertise to create challenging technical tasks, evaluate model-generated work, identify failures, and establish objective standards for correct solutions. This role is intended for candidates who have already completed substantive AI-training work.

\n

What You’ll Work On

\n
    \n
  1. Create realistic machine learning engineering tasks that challenge advanced AI models.
    2. \n
  2. Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
    3. \n
  3. Evaluate model-generated code, experiments, analyses, and technical deliverables.
    4. \n
  4. Identify issues involving data leakage, incorrect validation, unstable training, performance, or reproducibility.
    5. \n
\n

Required Qualifications

\n
    \n
  1. Prior paid experience training, evaluating, benchmarking, or improving AI systems.
    2. \n
  2. Strong practical proficiency with Python.
    3. \n
  3. Hands-on experience using PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, LightGBM, or comparable ML tools.
    4. \n
\n

Public Proof of Skill

\n

Strong applicants should provide evidence such as:

\n
    \n
  1. GitHub repositories or public model implementations
    2. \n
  2. Kaggle, DrivenData, AIcrowd, or similar competition results
    3. \n
  3. Hugging Face models or datasets
    4. \n
  4. Research publications or technical reports
    5. \n
  5. Open-source ML contributions
    6. \n
  6. Public benchmarks, experiments, or technical projects
    7. \n
\n

Preferred Qualifications

\n
    \n
  1. Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
    2. \n
  2. Experience reviewing other AI trainers’ technical submissions.
    3. \n
  3. Kaggle Expert, Master, or Grandmaster status, or strong results in another technical competition.
    4. \n
  4. Publications or open-source contributions in machine learning.
    5. \n
  5. Prior experience with long-horizon or agentic AI evaluations.
    6. \n
  6. Availability to begin immediately.
    7. \n
\n

Application Process

\n
    \n
  1. Submit your application, AI-training experience, and public technical evidence.
    2. \n
  2. Complete an AI interview containing technical and integrity checks.
    3. \n
  3. Complete a hiring-manager review.
    4. \n
  4. If selected, complete project onboarding and begin your first assigned task.
    5. \n
\n

Compensation

\n
    \n
  1. Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.
    2. \n
\n

Originally posted on Himalayas

\n

Source: this listing was published by Himalayas. View the original posting and apply — applications are handled there, not on this site.

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