About the Role
What if your deep knowledge of machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason and problem-solve?
We're looking for data scientists with graduate-level expertise to help train and evaluate cutting-edge AI models. You'll design complex technical challenges, author rigorous solutions, and audit AI-generated code — exposing model weaknesses and pushing the boundaries of what AI can reason through.
This is a fully remote, flexible contract role. No prior AI experience needed — just a strong command of data science and a sharp eye for technical precision.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
What You'll Do
- Design Advanced Challenges: Develop complex, domain-spanning data science problems — covering hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
- Author Ground-Truth Solutions: Write rigorous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as definitive reference answers
- Audit AI-Generated Code: Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness
- Refine AI Reasoning: Identify logical failures in AI outputs — such as data leakage, overfitting, or improper handling of imbalanced datasets — and provide structured, actionable feedback to improve model reasoning
- Work Independently: Complete task-based assignments asynchronously on your own schedule
Who You Are
- Currently pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
- Strong foundational knowledge across core data science domains — supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
- Able to communicate complex algorithmic and statistical concepts clearly and precisely in writing
- Highly detail-oriented when it comes to code syntax, mathematical notation, and the validity of statistical conclusions
- Self-motivated and reliable when working independently
- No prior AI training or annotation experience required
Nice to Have
- Experience with data annotation, data quality evaluation, or model evaluation workflows
- Familiarity with production-level data science practices — MLOps, CI/CD for models, or similar
- Exposure to academic or applied research in machine learning or statistics
- Prior work in technical writing, code review, or curriculum design
Why Join Us
- Work directly alongside leading AI research labs on frontier model development
- Fully remote and flexible — work when and where it suits you
- Freelance autonomy with consistent, meaningful, technically engaging work
- Build a portfolio of high-impact AI training contributions at the cutting edge of the field
- Potential for ongoing contracts and expanded project opportunities as new work launches