Remote Data Science & AI Benchmarking Expert — Contract

Alignerr Corp.

New Zealand

Remote

NZD 148,000 - 295,000

Full time

4 hours ago
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Benefits offered by this job

Fully remote
Async work schedule

Job summary

Alignerr is seeking a Data Science Expert – AI Content Specialist for a remote hourly contractor role. You will stress-test and improve cutting-edge AI models, designing challenges, authoring benchmarks, and auditing code for correctness and efficiency.

You will work independently, collaborating with research teams while maintaining high standards and delivering rigorous, transparent feedback on model reasoning and failure modes.

Qualifications

  • Master's or PhD in Data Science, Statistics, Computer Science or related field.
  • Strong foundation in supervised/unsupervised learning, deep learning, NLP, and big data tech.
  • Clear written communication of complex concepts and results.
  • Self-directed, independent technical work with precise review.

Responsibilities

  • Design advanced, domain-specific DS challenges across methods like Bayesian inference and CV.
  • Author rigorous ground-truth solutions with scripts, SQL queries, and derivations.
  • Audit AI outputs with Scikit-learn, PyTorch, and TensorFlow for correctness and efficiency.
  • Identify data leakage, overfitting, and imbalance; provide structured feedback.
  • Document failure modes to help teams harden model performance.

Skills

Data Science
Statistics
Machine Learning
NLP
Big data

Education

Master's or PhD in Data Science/Statistics/CS

Tools

Scikit-learn
PyTorch
TensorFlow
SQL
Spark
Hadoop

Job description

Alignerr is seeking a Data Science Expert – AI Content Specialist for a remote hourly contractor role. You will stress-test and improve cutting-edge AI models, designing challenges, authoring benchmarks, and auditing code for correctness and efficiency.

You will work independently, collaborating with research teams while maintaining high standards and delivering rigorous, transparent feedback on model reasoning and failure modes.

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