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About the Role

Imagine leveraging your expertise in machine learning, statistical inference, and data engineering to directly influence the reasoning and responsiveness of the world’s most advanced AI systems. As a Data Scientist at Alignerr, you will challenge, evaluate, and enhance state-of-the-art AI models by pushing their boundaries, identifying weaknesses, and contributing to best-in-class solutions. This is a fully remote, flexible contracting opportunity that allows you to apply your deep technical skills on the cutting edge of AI development.

Responsibilities

Create complex, domain-specific data science challenges involving hyperparameter tuning, Bayesian inference, cross-validation, dimensionality reduction, and related topics.
Develop detailed, stepwise technical benchmark solutions including Python and R scripts, SQL queries, and mathematical proofs to serve as gold standards for AI responses.
Assess AI-generated code using frameworks such as Scikit-Learn, PyTorch, and TensorFlow for accuracy, efficiency, and correctness.
Detect logical errors in AI outputs, such as data leakage, overfitting, and poor handling of imbalanced data, providing constructive feedback to improve AI reasoning.
Document failure scenarios by rigorously stress-testing models across machine learning concepts, statistical inference, neural networks, and data engineering processes.

Requirements

Currently pursuing or holding a Master's or PhD degree in Data Science, Statistics, Computer Science, or another quantitative discipline emphasizing data analysis.
Strong understanding of supervised and unsupervised learning, deep learning, big data technologies like Spark or Hadoop, and/or natural language processing.
Excellent written communication skills, able to clearly explain intricate algorithmic and statistical concepts.
Highly detail-oriented with the ability to spot errors in coding syntax, mathematical expressions, and statistical interpretations.
Self-motivated and dependable when managing complex technical tasks independently.
No previous experience working in the AI industry is necessary.

Preferred Qualifications

Experience in data annotation, quality control, or model assessment systems.
Knowledge of production-level data science workflows such as MLOps, continuous integration/deployment (CI/CD) for models, and experiment tracking.
Understanding of prompt engineering and AI benchmarking practices.

Why Join Us

Engage with the forefront of AI innovation alongside leading research professionals.
Benefit from completely remote and asynchronous working arrangements, allowing you to work whenever and wherever you prefer.
Enjoy freelance flexibility paired with challenging and meaningful technical assignments.
Gain hands-on experience with the most cutting-edge language models available.
Opportunities for contract extensions as new AI projects become available.

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