Machine Learning Engineer – ML Evaluation & Experiment Design

Anyone AI is recruiting experienced Machine Learning Engineers for a specialized project focused on reviewing and evaluating machine learning challenges used in AI model training and evaluation.

The work involves analyzing ML experiments, datasets, metrics, and pipelines to determine whether challenges are technically sound, reproducible, appropriately difficult, and genuinely require strong machine learning reasoning.

You'll review ML challenges involving:

• Experiment design and model selection

• Small and synthetic datasets

• Data quality and preprocessing

• Distribution shift and data contamination

• Label noise and feature leakage

• Model evaluation and metric selection

• Hyperparameter tuning

• Train / validation / test methodology

• Reproducibility and deterministic pipelines

• Statistical significance of model improvements

A key part of the role is determining whether a challenge actually rewards good ML reasoning, rather than simply being solvable through brute-force model selection or large hyperparameter searches.

• 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

Work Type: Remote
Engagement: Part-time, project-based consulting
Focus: Applied machine learning, experiment design, data quality, and model evaluation

This role is a strong fit for ML engineers who enjoy debugging experiments, understanding why models succeed or fail, identifying problems in datasets and evaluation pipelines, and designing rigorous machine learning experiments.