---
title: "Machine Learning Engineer – ML Evaluation & Experiment Design"
company: "Anyone AI"
company_url: "https://www.remjobs.works/companies/anyone-ai"
url: "https://www.remjobs.works/job/anyone-ai-machine-learning-engineer-ml-evaluation-experiment-design-085cd3fe-5653-4275-ab14-0410cff97dc0"
apply_url: "https://jobs.ashbyhq.com/anyone-ai/513aea4e-018c-46c7-8690-a4555979b8b0"
workplace: remote
location: "Argentina - Fully Remote"
remote_scope: "Argentina - Fully"
employment_type: contract
seniority: mid
role: ai-machine-learning
region: latin-america
salary: "$65 per hour"
date_posted: 2026-09-15T14:12:41.807Z
first_seen_by_remjobs: 2026-09-15T19:00:41.359Z
---

# Machine Learning Engineer – ML Evaluation & Experiment Design

**Anyone AI** · Argentina - Fully Remote

Salary: $65 per hour

Apply: https://jobs.ashbyhq.com/anyone-ai/513aea4e-018c-46c7-8690-a4555979b8b0

## About Anyone AI

Impulsa tu carrera en AI. Desarrolla tus habilidades con un entrenamiento intensivo y práctico, dictado por expertos, y accede al mercado de la AI.

## 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

- Reproducibility and deterministic pipelines

- Statistical significance of model improvementsA 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.**

---

Source: Anyone AI's own career page, read by RemJobs. Canonical HTML version: https://www.remjobs.works/job/anyone-ai-machine-learning-engineer-ml-evaluation-experiment-design-085cd3fe-5653-4275-ab14-0410cff97dc0