Data Scientist (Masters) — AI Data Trainer

About the role

This contract opportunity is for an experienced data scientist who wants to help improve advanced AI systems by challenging their reasoning, testing their limits, and creating high-quality benchmark solutions. The work is fully remote and flexible, with technical tasks that sit at the frontier of AI model development.

Role details

• Company: Alignerr
• Engagement type: Hourly contract
• Work setting: Remote
• Weekly commitment: 10 to 40 hours

What you will do

• Build advanced, domain-specific challenge problems in data science, including topics such as hyperparameter tuning, Bayesian methods, cross-validation design, dimensionality reduction, and related areas.
• Prepare authoritative solutions with clear, step-by-step reasoning, including Python or R code, SQL, and mathematical workings that can be used as the standard for comparison.
• Review AI-generated code for correctness, efficiency, and technical soundness using tools and libraries such as Scikit-Learn, PyTorch, and TensorFlow.
• Spot weaknesses in AI outputs, such as data leakage, overfitting, or poor handling of imbalanced datasets, and provide structured feedback that improves reasoning quality.
• Stress-test responses across machine learning theory, statistical inference, neural network design, and data engineering workflows, while documenting failure patterns and missing coverage.

Who this role suits

• People who are currently studying for, or already hold, a Master's or PhD in Data Science, Statistics, Computer Science, or another quantitative discipline with a strong data-analysis focus.
• Candidates with solid grounding in supervised and unsupervised learning, deep learning, large-scale data tools such as Spark or Hadoop, or natural language processing.
• Individuals who can explain technical and statistical ideas clearly and precisely in writing.
• Detail-focused professionals who notice mistakes in code syntax, mathematical notation, and statistical interpretation.
• Self-motivated contributors who work well independently on technical assignments.
• No previous AI-industry background is required.

Nice to have

• Experience in data annotation, data quality checks, or model evaluation workflows.
• Exposure to production data science practices such as MLOps, CI/CD for models, and experiment tracking.
• Familiarity with prompt engineering or AI benchmarking approaches.

Why consider this opportunity

• Contribute to cutting-edge AI work alongside leading research teams.
• Enjoy a fully remote, asynchronous schedule that lets you choose when and where you work.
• Benefit from freelance-style independence while doing technically engaging work.
• Work directly with advanced language models.
• There may be opportunities for ongoing renewals as new AI projects begin.