Remote AI Data Trainer — Design & Evaluate ML Challenges

Alignerr Corp.; Adelaide; On-site; AUD 83,000 - 165,000; Part time

Job summary

Alignerr seeks a Data Scientist (Masters) to train and evaluate state-of-the-art AI models from a remote setting. You will craft advanced challenges, author ground-truth solutions with Python/R scripts, and audit AI-generated code for correctness and efficiency.

You will identify reasoning failures and document edge cases to improve model thinking and reliability, collaborating asynchronously with the AI research team.

Qualifications

• Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative field.
• Strong foundation across supervised/unsupervised learning, deep learning, statistical inference, or big data technologies like Spark and Hadoop.
• Ability to clearly communicate complex algorithms and statistics in writing.
• Self-directed and capable of independent technical work.

Responsibilities

• Design Advanced Challenges — create complex data science problems across ML topics.
• Author Ground-Truth Solutions — write rigorous, step-by-step technical solutions with code and derivations.
• Audit AI-Generated Code — evaluate outputs for correctness and efficiency using ML libraries.
• Identify Reasoning Failures — document logical errors and provide structured feedback.
• Improve Model Reasoning — document failure modes to harden AI systems.

Skills

Machine Learning

Statistical Modeling

Data Analysis

Python

R

SQL

Independent Work

Education

Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative field

Quantitative field emphasis

Tools

Scikit-Learn

PyTorch

TensorFlow

Spark

Hadoop

Job description

Alignerr seeks a Data Scientist (Masters) to train and evaluate state-of-the-art AI models from a remote setting. You will craft advanced challenges, author ground-truth solutions with Python/R scripts, and audit AI-generated code for correctness and efficiency.

You will identify reasoning failures and document edge cases to improve model thinking and reliability, collaborating asynchronously with the AI research team.