AI Summary
Design advanced AI challenges, author rigorous solutions, and audit AI-generated code. Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field. Strong foundational knowledge across supervised/unsupervised learning, deep learning, big data technologies, or NLP.
Key Highlights
Design Advanced Challenges
Author Ground-Truth Solutions
Audit AI-Generated Code
Key Responsibilities
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
Technical Skills Required
Benefits & Perks
Fully remote
Flexible contract role
10–40 hours/week
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
Job Description
Data Scientist (Masters) — AI Data Trainer
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
- 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
Interested in remote work opportunities in Data Science? Discover Data Science Remote Jobs featuring exclusive positions from top companies that offer flexible work arrangements.
- 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
Browse our curated collection of remote jobs across all categories and industries, featuring positions from top companies worldwide.
- 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
- 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
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