Remote ML Experiment Architect & Evaluation Engineer

Anyone AI Inc.; Ciudad de México; Remote; MXN 1,011,000 - 2,023,000; Part time

Job summary

Anyone AI Inc. is seeking experienced Machine Learning Engineers for a remote, part-time, project-based consulting role focused on reviewing and evaluating ML challenges used in model training and evaluation.

You will analyze experiments, datasets, metrics, and pipelines to ensure soundness, reproducibility, and meaningful ML reasoning. You will design experiments, select models, tune hyperparameters, and evaluate performance with rigorous statistical considerations.

Qualifications

• Hands-on 3+ years of applied machine learning experience.
• Strong understanding of ML evaluation metrics and appropriate contexts.
• Ability to provide clear written feedback on technical problems.
• Experience debugging ML workloads across CPU and GPU environments.

Responsibilities

• Reviewing ML challenges and determining whether they are well designed and technically solvable.
• Evaluating whether datasets contain meaningful and learnable signals.
• Identifying unintended shortcuts or artifacts in synthetic datasets.
• Determining whether tasks require genuine diagnosis of the underlying ML problem.
• Reviewing evaluation metrics and improvement thresholds.
• Detecting metric gaming, data leakage, and evaluation flaws.
• Verifying reproducibility across the complete data model evaluation pipeline.
• Assessing whether challenge difficulty is appropriately calibrated.
• Providing clear recommendations for improving, recalibrating, or excluding problematic tasks.

Skills

ML experiment design

Model selection

Hyperparameter tuning

Model evaluation

Data preprocessing

Train/validation/test splits

Data leakage detection

Statistical significance

Job description

Anyone AI Inc. is seeking experienced Machine Learning Engineers for a remote, part-time, project-based consulting role focused on reviewing and evaluating ML challenges used in model training and evaluation.

You will analyze experiments, datasets, metrics, and pipelines to ensure soundness, reproducibility, and meaningful ML reasoning. You will design experiments, select models, tune hyperparameters, and evaluate performance with rigorous statistical considerations.