About the Role
What if your expertise in 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 training to challenge, audit, and refine cutting-edge AI models — exposing their blind spots and building the ground-truth solutions that make them smarter.
This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep, rigorous command of data science and a sharp eye for technical accuracy.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
What You'll Do
- Design Complex Challenges — Develop advanced data science problems spanning 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
- Sharpen AI Reasoning — Identify logical failures in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and deliver structured feedback that directly improves how these models think
- Document Failure Modes — Systematically record how and where advanced language models break down on data science tasks, helping research teams harden model reliability
Who You Are
- Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative discipline with a strong data focus
- Deeply fluent in core data science concepts: supervised and unsupervised learning, deep learning, statistical inference, and big data technologies
- Able to translate complex algorithmic concepts and statistical results into clear, precise written explanations
- Exceptionally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical conclusions that others miss
- Comfortable working independently and asynchronously without hand-holding
- No prior AI or data annotation experience required
Nice to Have
- Experience with data annotation, data quality evaluation, or AI model assessment workflows
- Proficiency in production data science environments — MLOps, CI/CD for models, model monitoring
- Familiarity with NLP, computer vision, or large-scale distributed computing (Spark, Hadoop)
- Background in technical writing, research, or academic publishing
Why Join Us
- Work directly with industry-leading AI research teams and cutting-edge language models
- Fully remote and flexible — structure your hours around your life
- Freelance autonomy with meaningful, intellectually stimulating work
- Contribute to AI development that shapes the future of data science reasoning at scale
- Potential for ongoing contract renewals as new projects launch