Machine Learning Engineer

Type: Freelance / ContractLocation: Fully RemoteHours: Around 20 hours/week, with flexible schedulingProcess: Short screening call → Technical assessment → OnboardingThe workYou'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 answerAn evaluation that reports misleading numbers because of a subtle data errorA sluggish data pipeline that has to run much faster without changing a single resultThe 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 needA degree in computer science, engineering, maths or a related subjectAt least 4 years of full-time experience in ML engineering, applied ML or data science engineeringStrong production Python, including pandas, NumPy, scikit-learn and at least one of XGBoost, LightGBM or CatBoostConfidence with Docker, and with finding your way around large codebases spread across many filesAn instinct for spotting leakage, flattering metrics and solutions that only look rightPrevious 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 offerBetween $30 and $100 per hour, depending on experienceFully remote, with hours that fit around youTechnically demanding work at the sharp end of AI developmentHow to applySend 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.