🕒 2 days ago
Join a modelling residency embedded in production and research projects, developing adaptive algorithms, optimizing efficiency across the ML stack, and using real-world product feedback to measure impact.
ML Intern
🕒 2 days ago
PythonPytorchTensorflowScikit-learn
📜 Description
- Experiment with language model configurations to identify optimization opportunities.
- Build and maintain data pipelines for model training and evaluation.
- Analyze metrics to assess model quality and efficiency.
- Contribute to tools that monitor and optimize model behavior.
- Track machine learning and language model research and apply relevant findings to product improvements.
🛠️ Requirements
- A degree in computer science, data science, engineering, mathematics, or a related field, or equivalent practical experience.
- Hands-on experience building or training machine learning models with Python and frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Knowledge of machine learning, statistics, model evaluation, and optimization techniques.
- Familiarity with large language models, natural language processing, and prompt design or tuning.
- Experience building machine learning data pipelines and analyzing model performance metrics.
- Experience contributing to tools or monitoring systems for tracking model performance is helpful.
Full job description
About the Role
Join a machine learning team focused on improving large language model efficiency and quality. You will help develop and evaluate approaches that reduce token usage while maintaining strong output quality.
What You'll Do
Experiment with language model configurations to identify optimization opportunities.
Build and maintain data pipelines for model training and evaluation workflows.
Analyze performance metrics to assess model quality and efficiency.
Contribute to internal tools that monitor and optimize model behavior.
Follow current machine learning and language model research and apply relevant findings to product improvements.
What We're Looking For
A degree in computer science, data science, engineering, mathematics, or a related field, or equivalent practical experience.
Hands-on experience building or training machine learning models with Python and frameworks such as PyTorch, TensorFlow, or scikit-learn.
Knowledge of machine learning, statistics, model evaluation, and optimization techniques.
Familiarity with large language models, natural language processing, and prompt design or tuning.
Experience building machine learning data pipelines and analyzing model performance metrics.
Experience contributing to tools or monitoring systems for tracking model performance is helpful.
Location
Remote, full-time.
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