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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 Tensor Flow 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
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