Machine Learning Engineer – ML Evaluation & Experiment Design

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.

What We’re Looking For

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

Ability to identify:

Data leakage

Label noise

Distribution shift

Spurious correlations

Feature leakage

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 Have

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

What You’ll Be Responsible For

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

Engagement

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.