Data Scientist – AI Model Training & Evaluation

Company: Alignerr

Location: Atlanta, GA (Remote)

Type: Full-time

Remote: Yes

Posted: 2026-05-04

About this role

About The Role
We're looking for data scientists based in Germany to help train, evaluate, and improve next-generation AI systems. Your quantitative expertise will directly influence how state-of-the-art AI models reason, analyze, and communicate — making a real and lasting impact on the future of artificial intelligence.

  • Organization: Alignerr
  • Type: Hourly Contract
  • Location: Remote
  • Commitment: 10–40 hours/week

What You'll Do

  • Evaluate AI model outputs for accuracy, reasoning quality, and statistical soundness
  • Design and apply data-driven evaluation criteria and scoring rubrics
  • Analyze patterns in AI-generated responses to identify systematic errors or biases
  • Create high-quality training data including prompts, solutions, and annotations in your areas of expertise
  • Provide structured, detailed feedback to improve model performance across data science and analytical tasks
  • Review AI-generated code, visualizations, and statistical analyses for correctness
  • Work independently and asynchronously on your own schedule

Who You Are

  • Degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field (MS or PhD preferred)
  • Strong foundation in statistics, probability, and machine learning concepts
  • Proficient in Python, R, SQL, or similar data analysis tools
  • Experienced with data wrangling, exploratory data analysis, and model evaluation
  • Excellent analytical thinking with sharp attention to detail
  • Strong written communication in English — able to explain complex technical concepts clearly and simply
  • Self-motivated and comfortable working independently

Nice to Have

  • Experience with deep learning frameworks such as PyTorch or TensorFlow
  • Familiarity with NLP, large language models, or AI evaluation workflows
  • Published research or industry experience in applied machine learning
  • Background in A/B testing, causal inference, or experimental design

Why Join Us

  • Work on cutting-edge...

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