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AI Trainer (ML Engineering)

micro1

LocationFully remote; global listing, but country eligibility must be checked on the official posting.
SeniorityUnspecified
Companymicro1
Verified recentlyChecked today
Compensation

$125k-$250k

Salary details are shown when available from the source listing. Sign in before applying so the role can be reviewed against your resume, salary goals, seniority, timezone, and location eligibility.

Requirements and working style

Decision details from the source listing

Schedule

flexible

These fields are normalized from the employer's text. Confirm details on the employer site before applying.

WFH.team analysis

What this posting tells you

Remote contractor role creating and evaluating machine-learning engineering tasks for AI systems. The listing states pay of $60–$120/hour equivalent, paid per accepted task; task availability and completion time may vary. It requires prior paid AI-training or model-evaluation experience but gives no minimum years. Global eligibility is subject to confirmation on the official posting.

Role lane

Backend, Customer success, Data, Operations, Product, Software, Customer support

Where you can work

Eligible countries are not stated.

Working hours

Global

Arrangement

Unspecified · Contract · Contractor

Required signals
Prior paid experience training, evaluating, benchmarking, or improving AI systemsPythonHands-on experience with PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, LightGBM, or comparable ML toolsMachine learning engineering
Preferred signals
Creating AI-training tasks, reference answers, rubrics, graders, or benchmarksReviewing other AI trainers’ technical submissionsKaggle Expert, Master, or Grandmaster status, or strong technical competition resultsMachine learning publications or open-source contributionsLong-horizon or agentic AI evaluationsPublic technical evidence such as repositories, competition results, models, datasets, publications, or benchmarks
Confirm before applying
  • Eligible hiring countries are not stated
Market context

Backend hiring on WFH.team

4,881active related roles
1,420new in the latest period
200.9jobs per 100 candidates
$182kmedian of comparable listed ranges

This role's listed pay is near the median among 200 comparable roles shown here. Category counts come from WFH.team's latest published remote job market snapshot.

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Skills and signals
Prior paid experience training, evaluating, benchmarking, or improving AI systemsPythonHands-on experience with PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, LightGBM, or comparable ML toolsMachine learning engineeringCreating AI-training tasks, reference answers, rubrics, graders, or benchmarksReviewing other AI trainers’ technical submissionsKaggle Expert, Master, or Grandmaster status, or strong technical competition resultsMachine learning publications or open-source contributionsLong-horizon or agentic AI evaluationsPublic technical evidence such as repositories, competition results, models, datasets, publications, or benchmarksFully remote; global listing, but country eligibility must be checked on the official posting.
Job description

AI Trainer (ML Engineering) at 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

Create realistic machine learning engineering tasks that challenge advanced AI models.

Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.

Evaluate model-generated code, experiments, analyses, and technical deliverables.

Identify issues involving data leakage, incorrect validation, unstable training, performance, or reproducibility.

Required qualifications

Prior paid experience training, evaluating, benchmarking, or improving AI systems.

Strong practical proficiency with Python.

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

GitHub repositories or public model implementations

Kaggle, DrivenData, AIcrowd, or similar competition results

Hugging Face models or datasets

Research publications or technical reports

Open-source ML contributions

Public benchmarks, experiments, or technical projects

Preferred qualifications

Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.

Experience reviewing other AI trainers’ technical submissions.

Kaggle Expert, Master, or Grandmaster status, or strong results in another technical competition.

Publications or open-source contributions in machine learning.

Prior experience with long-horizon or agentic AI evaluations.

Availability to begin immediately.

Application Process

Submit your application, AI-training experience, and public technical evidence.

Complete an AI interview containing technical and integrity checks.

Complete a hiring-manager review.

If selected, complete project onboarding and begin your first assigned task.

Compensation

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

Company context

Working remotely at micro1

micro1 builds human-expertise data and evaluation infrastructure for AI models and agents. It recruits core-team employees and domain experts for AI training projects.

Headquarters
San Francisco Bay Area, California, United States
Founded
2022
Remote policy

Expert opportunities are remote and can be flexible. International applicants are considered, subject to country exclusions and each project's requirements. Core-team work arrangements vary by opening; check the individual posting.

Application process

Apply through the official posting. Expert applicants complete an AI interview and skills certification, followed by project selection and onboarding. Certification does not guarantee a project or immediate work.

Research micro1