Machine Learning Engineer – ML Evaluation & Experiment Design

Anyone AI Inc.; Argentina; A distancia; ARS 1.200.000 - 2.000.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Remote work

Part-time project-based consulting

Descripción de la vacante

Anyone AI Inc. is seeking experienced Machine Learning Engineers to review and evaluate ML challenges used in AI model training and evaluation. You will analyze experiments, datasets, metrics, and pipelines to determine technical soundness and reproducibility.

The role focuses on ensuring challenges reward strong ML reasoning rather than brute-force model tuning, with a remote, project-based, part-time engagement.

Formación

• 3+ years hands-on applied machine learning experience.
• Strong ability to analyze ML challenges, datasets, and evaluation pipelines.
• Experience identifying data leakage, distribution shift, and spurious correlations.
• Ability to provide clear, written feedback on technical problems.

Responsabilidades

• Review ML challenges to ensure they are well designed and solvable.
• Evaluate whether datasets contain meaningful signals and learnable patterns.
• Identify shortcuts or artifacts in synthetic datasets.
• Verify reproducibility across the data–model–evaluation pipeline.
• Provide actionable recommendations to improve or recalibrate tasks.

Conocimientos

Applied ML experience

Data preprocessing

ML evaluation metrics

Experiment design

Debugging experiments

Descripción del empleo

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.

What You’ll Work On

You’ll review ML challenges involving:

• Small and synthetic datasets
• Data quality and preprocessing
• Distribution shift and data contamination
• Label noise and feature leakage
• Model evaluation and metric selection
• 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.

What We’re Looking For

3+ years of hands-on applied machine learning experience

Strong experience with:

• Data preprocessing and validation
• Strong understanding of train, validation, and test splits
• Ability to identify:
• Data leakage
• Distribution shift
• Spurious correlations
• Feature leakage
• 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
• 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.