Machine Learning Engineer – ML Evaluation & Experiment Design
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Before you apply
LocationArgentina - Fully Remote
Work moderemote
Typecontract
SeniorityMid
Experience3+ yrs
DepartmentSoftware Engineering
Company size1,001–5,000 people
PostedSep 15, 2026 · 1mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
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
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
Skills
Python
GPU
CPU
About the role
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:
•
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
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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:
•
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.