DevFixr
Freelance Machine Learning Engineer: Build Coding Challenges for AI Training
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Freelance Machine Learning Engineer: Build Coding Challenges for AI Training
Remote · Flexible hours · Freelance contract via DevFixr
DevFixr is recruiting experienced ML engineers for a client that creates training material for AI coding models.
The Work
You'll build realistic machine learning engineering problems that AI models learn from. Each one is a small but genuine codebase with something wrong in it, packaged in Docker, plus an automated grader that checks whether the model really solved it. Examples of what you might build:
- A model that looks excellent in testing but falls apart in production, because one of its features quietly gives away the answer
- An evaluation that reports misleading numbers because of a subtle data error
- A sluggish data pipeline that has to run much faster without changing a single result
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The most interesting part is the grading. Any correct fix has to pass, however it's written, while shortcuts, hardcoded answers and faked results have to fail.
What You'll Need
- A degree in computer science, engineering, maths or a related subject
- At least 4 years of full-time experience in ML engineering, applied ML or data science engineering
- Strong production Python, including pandas, NumPy, scikit-learn and at least one of XGBoost, LightGBM or CatBoost
- Confidence with Docker, and with finding your way around large codebases spread across many files
- An instinct for spotting leakage, flattering metrics and solutions that only look right
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Previous AI-training work is a plus, not a requirement. If you've spent years catching the bugs other people missed, you'll probably be good at this.
What's on Offer
- Between $30 and $100 per hour, depending on experience
- Fully remote, with hours that fit around you
- Technically demanding work at the sharp end of AI development
How to Apply
Send us your CV. Shortlisted candidates are invited to a 30-minute technical interview, where you'll review an example task and talk through how you'd design one.
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