Machine Learning Engineer – ML Evaluation & Experiment Design

Anyone AI

11d • Remote

About The Position

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.

Requirements

• 3+ years of hands-on applied machine learning experience
• Strong experience with ML experiment design
• Strong experience with model selection
• Strong experience with hyperparameter tuning
• Strong experience with model evaluation
• Strong experience with data preprocessing and validation
• Strong understanding of train, validation, and test splits
• Ability to identify data leakage
• Ability to identify label noise
• Ability to identify distribution shift
• Ability to identify spurious correlations
• Ability to identify feature leakage
• Ability to identify data contamination
• Experience evaluating whether performance improvements are statistically meaningful rather than random fluctuations
• Strong understanding of ML evaluation metrics and when different metrics are appropriate
• Experience debugging ML workloads across CPU and GPU environments
• Ability to analyze technical problems and provide clear written feedback

Nice To Haves

• Experience creating or participating in Kaggle, DrivenData, or similar ML competitions
• Experience designing benchmark datasets or ML challenges
• Background in data-centric AI or dataset quality
• Experience with synthetic data generation and validation
• Familiarity with statistical testing, confidence intervals, and effect sizes
• Experience with ML evaluation pipelines, RLHF, or AI model evaluation
• Experience developing ML curricula or technical assessments
• Understanding of common ML failure modes such as: Shortcut learning, Spurious correlations, Goodhart’s Law, Simpson’s paradox, Metric gaming

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 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

What This Job Offers

• Job Type: Part-time
• Career Level: Senior
• Education Level: No Education Listed