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 advanced degrees to work alongside leading AI research labs — designing expert-level challenges, authoring rigorous solutions, and auditing AI-generated code to make models smarter, more accurate, and more reliable.
This is a fully remote, flexible contract role. No prior AI industry experience required — just serious domain expertise and a sharp analytical mind.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
What You'll Do
- Design Advanced Challenges — Create complex, domain-spanning data science problems covering hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
- Author Ground-Truth Solutions — Develop rigorous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the gold standard for AI training
- Audit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness
- Refine AI Reasoning — Identify logical flaws such as data leakage, overfitting, or improper handling of imbalanced datasets and provide structured feedback to sharpen model thinking
- Document Failure Modes — Probe advanced language models on topics like neural network architectures and data engineering pipelines, capturing and reporting every reasoning gap
Who You Are
- Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong data analysis focus
- Strong foundational knowledge across supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
- Able to communicate highly technical algorithmic and statistical concepts clearly and concisely in writing
- Exceptionally detail-oriented when reviewing code syntax, mathematical notation, and the validity of statistical conclusions
- Self-directed and comfortable working independently on an async schedule
- No prior AI or data annotation experience required
Nice to Have
- Experience with data annotation, data quality assurance, or AI evaluation systems
- Proficiency in production-level data science workflows — MLOps, CI/CD for models, or similar
- Familiarity with model evaluation frameworks or benchmarking methodologies
Why Join Us
- Work directly on cutting-edge AI projects alongside world-leading research labs
- Fully remote and async — work when and where it suits you
- Freelance autonomy with meaningful, intellectually stimulating work
- Direct, hands-on engagement with industry-leading large language models
- Potential for ongoing contract renewals as new projects launch