AI, ML & Data Science Expert →

IsoData; United States; Remote; USD 207,000 - 413,000; Full time

Job summary

IsoData is seeking contributors to create, grade, and review ML data-analysis tasks from home in the United States. You’ll write tasks with checkable answers and embedded pitfalls, grade responses, and review training or evaluation code for subtle bugs that affect results.

The role requires a quantitative degree, a hands-on ML project, and proficiency with Python and libraries like pandas, NumPy, scikit-learn, and PyTorch. Remote work is supported; apply as projects arise.

Qualifications

• You read and write Python with common data and ML libraries (pandas, NumPy, scikit-learn, PyTorch).
• One substantial ML or data project you did yourself, with results and checks.
• Ability to spot leaks, bad splits, and misleading metrics before trusting numbers.

Responsibilities

• Write data-analysis and modeling tasks with one checkable answer and a planted pitfall.
• Grade model answers on experiment design, metrics and statistical validity.
• Review training and evaluation code for bugs that quietly change results.
• Design rubrics for grading model outputs so graders reach the same score.
• Write reference notebooks showing the right analysis steps.
• Compare two model explanations and justify which is right and why.

Skills

Python

Data analysis

ML concepts

Education

Bachelors in CS/Stats

Masters or PhD preferred

Tools

pandas

NumPy

scikit-learn

PyTorch

Job description

Remote, United States. Project-based.

For people who build, train or evaluate models and can spot where an analysis quietly goes wrong. Create and grade tasks on model training, evaluation and data analysis.

Pay: $150 to $300 an hour, paid per accepted task. There is no guaranteed project, task volume, hours or pay.

You must live in the US, be at least 18 and be authorized to work in the United States. We accept applications on an ongoing basis.

What you would do

• Write data-analysis and modeling tasks with one checkable answer and a planted pitfall, such as a feature that leaks the label.
• Grade model answers on experiment design, metrics and statistical validity, and mark the first step that goes wrong.
• Review training and evaluation code, such as a PyTorch training loop or a scikit-learn pipeline, for bugs that quietly change the results.
• Design rubrics for grading model outputs, so different graders reach the same score.
• Write reference notebooks that show the right analysis step by step.
• Compare two model explanations of an ML idea, such as regularization or attention, and say which is right and why.

The minimum

• You study at or graduated from a college or university in a quantitative field, such as computer science, statistics, math or engineering.
• One substantial ML or data project you did yourself, with or without AI tools, that you can explain in depth, including how you checked the results.
• You read and write Python with common data and ML libraries, such as pandas, NumPy, scikit-learn or PyTorch.
• You check for leakage, bad splits and misleading metrics before you trust a number.

Ideal

• A degree in computer science, statistics or a related field; a master’s or PhD is even better.
• Research or production ML work, especially at an AI lab or AI company.
• Hands‑on work with language models: fine‑tuning, evaluation, red‑teaming or building graders.
• Papers, competition results, models or datasets others use.

This position covers

Machine learning engineering, Data science and analytics, Model evaluation, Deep learning, Language and vision models, Data pipelines.

IsoData provides equal opportunity to all applicants. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, citizenship status, age, disability, genetic information, veteran status or any other protected trait. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

IsoData recruits Experts for its own projects.