ML Experiment Validity Reviewer

ixolabs.ai; France; Hybride; EUR 90 000 - 114 000; Plein temps

Résumé du poste

ixolabs.ai is seeking specialists to audit machine-learning challenges, assess experiments and define defensible success criteria. Your work focuses on inspecting data, objectives and metrics to ensure meaningful results and reproducible checks.

You will identify leakage, weak baselines and faulty splits, then provide explicit, evidence-based feedback and conclusions within agreed scope.

Qualifications

• At least three years conducting applied ML experiments, including model choice, tuning, evaluation and experimental design.
• Recognize leakage, misleading metrics and faulty train/test/cross-validation splits.
• Reproduce results using relevant tools such as XGBoost, scikit-learn, TensorFlow or PyTorch.

Responsabilités

• Review the task's data, objective, experimental setup and metrics to check that the claimed result is meaningful.
• Identify leakage, weak baselines, unstable evaluation or other methodological issues that could mislead a benchmark.
• Write clear audit feedback that distinguishes an invalid task from a difficult but well-specified ML problem.

Connaissances

Applied ML experiments

Model evaluation

Experimental design

Leakage detection

Cross-validation

Reproducible results

Outils

XGBoost

scikit-learn

TensorFlow

PyTorch

Description du poste

IXO is engaging specialists to evaluate machine-learning challenges with valid experiments and defensible success criteria. Your contribution is technical work: make the reasoning inspectable, identify substantive errors and provide evidence that supports a reliable assessment.

Work you will do

• Review the task's data, objective, experimental setup and metrics to check that the claimed result is meaningful.
• Identify leakage, weak baselines, unstable evaluation or other methodological issues that could mislead a benchmark.
• Write clear audit feedback that distinguishes an invalid task from a difficult but well-specified ML problem.

Required background and routes

• At least three years conducting applied ML experiments, including model choice, tuning, evaluation and experimental design.
• Recognize leakage, misleading metrics and faulty train/test/cross-validation splits. Reproduce results using relevant tools such as XGBoost, scikit-learn, TensorFlow or PyTorch.

Preferred background

• Kaggle or other benchmark work, graduate research/publications, peer review or task grading. This route centers on experimental validity rather than LLM application construction.

Deliverables

Submit the completed technical artifact or assessment with its supporting evidence, explicit assumptions, reproducible checks where applicable, and concise reasons for each material judgment. Address review findings within the agreed scope.

Location and schedule

Regional eligibility: United States. Remote assignments are scheduled by agreement, with no guaranteed weekly volume. Availability planning can include 40 hours per week depending on the track. IXO confirms the applicable timing before work.

Pay and working terms

$75 $95/hr USD. The agreed hourly rate, scope, schedule and acceptance criteria are confirmed before work starts. Applying does not guarantee an assignment. Use public, licensed or otherwise authorized material only; do not submit confidential employer information, personal data or restricted research.