Machine Learning Engineers (AI)

Location: Remote from Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Portugal, Spain, or Uruguay

Salary: $65 per hour

Company

hirify.global is recruiting machine learning engineers for a project focused on reviewing and evaluating machine learning challenges used in AI model training and evaluation.

What you will do

• Review ML challenges, experiments, datasets, metrics, and pipelines for technical soundness, reproducibility, and appropriate difficulty.
• Evaluate whether datasets contain meaningful, learnable signals and whether tasks require genuine ML reasoning rather than brute-force model selection.
• Identify data leakage, label noise, distribution shift, spurious correlations, feature leakage, contamination, shortcuts, and evaluation flaws.
• Verify reproducibility across complete data-to-model-to-evaluation pipelines and debug workloads across CPU and GPU environments.
• Assess statistical significance, improvement thresholds, metric gaming, and the calibration of challenge difficulty.
• Provide clear recommendations to improve, recalibrate, or exclude problematic tasks.

Requirements

• 3+ years of hands-on applied machine learning experience.
• Strong experience with ML experiment design, model selection, hyperparameter tuning, model evaluation, data preprocessing, and validation.
• Strong understanding of train, validation, and test splits and appropriate ML evaluation metrics.
• Ability to identify data leakage, label noise, distribution shift, spurious correlations, feature leakage, and data contamination.
• Experience assessing statistical significance, confidence intervals, effect sizes, and whether performance improvements are meaningful.
• Ability to analyze technical problems and provide clear written feedback.

Nice to have

• Experience with Kaggle, DrivenData, or similar ML competitions.
• Experience designing benchmark datasets or ML challenges.
• Background in data-centric AI, dataset quality, synthetic data generation, ML evaluation pipelines, RLHF, or AI model evaluation.
• Experience developing ML curricula or technical assessments.
• Understanding of shortcut learning, Goodhart’s Law, Simpson’s paradox, and metric gaming.

Culture & Benefits

• Part-time, project-based consulting engagement.
• Remote work from the listed countries.
• Focus on applied machine learning, experiment design, data quality, and model evaluation.