AI Research Intern — Build Next-Gen LLM Routing Solutions

Mirakl---Labs

Paris

On-site

EUR 10,000 - 17,000

Full time

2 days ago
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Job summary

Mirakl's AI Research Internship in Paris (16th arrondissement) joins the R&D team led by Robin Vaysse, Senior AI Researcher. You will collaborate with 5 Research Engineers in a Data department of 70 people to push the boundaries of AI and machine learning powering Mirakl's products.

Your impact includes literature reviews of LLM routing, designing a routing method to choose language models, and evaluating on real product data for quality, cost and latency, with documentation for stakeholders.

Qualifications

  • Master's degree in applied mathematics or related field.
  • Solid grounding in statistics, machine learning, and deep learning.
  • Experience with data preparation techniques and feature engineering.
  • Demonstrated interest in research: reading academic papers and tracking state-of-the-art models.

Responsibilities

  • Conduct a structured literature review of LLM routing approaches to map the state of the art.
  • Design and implement a routing method that automatically selects the most suitable language model for a given task.
  • Evaluate your solution on real product data, measuring quality, cost, and latency.
  • Collaborate with experienced Research Engineers and Data Scientists to iterate on hypotheses and refine your approach.
  • Document findings and present results to technical and non-technical stakeholders.

Skills

Python
Machine learning
Deep learning
Data preparation
Pandas
PyTorch
TensorFlow
Transformers

Education

Master's degree (Bac+5) in applied mathematics

Tools

PyTorch
TensorFlow
Transformers
scikit-learn
Pandas

Job description

Mirakl's AI Research Internship in Paris (16th arrondissement) joins the R&D team led by Robin Vaysse, Senior AI Researcher. You will collaborate with 5 Research Engineers in a Data department of 70 people to push the boundaries of AI and machine learning powering Mirakl's products.

Your impact includes literature reviews of LLM routing, designing a routing method to choose language models, and evaluating on real product data for quality, cost and latency, with documentation for stakeholders.

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