Machine Learning Engineer

This is a fully remote position, open to applicants in New York.

📋 Description

• Assess the methodological integrity of applied machine learning experiments.

• Examine experimental hypotheses, assumptions, modeling decisions, and the rationale behind them.

• Evaluate train/test splits, cross-validation techniques, validation processes, datasets, and experimental protocols.

• Detect issues such as data leakage, target leakage, contamination, metric manipulation, overfitting, cherry-picking, and unreliable conclusions.

• Analyze model selection choices, hyperparameter tuning strategies, model comparisons, metrics, and statistical support.

• Reproduce or verify experimental outcomes and investigate any discrepancies.

• Review code, configurations, experimental settings, and corroborating evidence.

• Utilize implementations from PyTorch, TensorFlow, scikit-learn, and XGBoost.

• Evaluate applied machine learning tasks and benchmark-style challenges.

• Measure assigned machine learning tasks against defined technical criteria.

• Deliver clear written explanations and detailed technical feedback to support evaluation decisions.

• Differentiate authentic methodological issues from reasonable alternative strategies.

⛳️ Requirements

• Minimum of 3 years of practical experience in applied or experimental machine learning.

• Strong hands-on experience with experiment design, model selection, hyperparameter tuning, and evaluation methodologies.

• Profound understanding of data leakage, metric manipulation, train/test methodologies, and cross-validation best practices.

• Proficiency in standard machine learning frameworks, including PyTorch, TensorFlow, scikit-learn, or XGBoost.

• Excellent ability to evaluate machine learning assertions against experimental evidence.

• Comfortable with reproducing results and identifying discrepancies.

• Strong quantitative and analytical judgment skills.

• Ability to communicate clearly in writing and provide precise technical feedback.

• Experience in Kaggle, ML competitions, or benchmark challenges is a plus.

• Graduate research or publication experience in applied machine learning is preferred.

• Previous experience in task grading, technical peer review, or ML evaluation is advantageous.

• All work must be done without utilizing confidential or proprietary information from any employer, client, institution, or third party.

• H1-B and STEM OPT support is not available for this role.

🏝️ Benefits

• Part-time independent contractor position.

• Fully remote work within the United States.

• Flexible scheduling tailored to project needs.

• Project durations may be adjusted or concluded based on requirements and performance.