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Microsoft Data & Applied Scientist II 
Spain, Catalonia, Barcelona 
307105668

10.09.2024

Azure AI Search provides a secure, at scale, search engine over user-owned data. The data and applied science team develops the machine learning components that power the search engine, both for traditional and generative AI scenarios such as RAG (retrieval-augmented generation). Our goal is to deliver high quality search results for very different industries, corpus sizes and scenarios, and our work includes multiple aspects, among which:

  • Training and fine-tuning of deep learning models, including language models, often with tight latency constraints;
  • Collection, generation and filtering of training and evaluation data;
  • Metrics development;Keeping up with research and industry trends.

Here is an example of our work:

Required Qualifications:

  • Bachelors, Masters or advanced degree in Computer Science or related field (including Mathematics and Physics).
  • Relevant industry experience in applying Machine Learning techniques.
  • Relevant experience in coding in Python, C#, Java or C++  .

Preferred Qualifications:

  • MS or PhD in computer science or related field.
  • Experience with machine learning frameworks such as PyTorch, ONNX, etc...
  • Experience with deep model training and evaluation.
  • Experience with using large language models.
  • Customer focused, strategic, drives for results, is self-motivated, and has a propensity for action.
  • Problem solver: ability to solve problems that the world has not solved before.
Responsibilities

As part of the team, you will be responsible for different aspects, such as:

  • Train, fine-tune, domain adapt and distill large scale NLP models for real-world large-scale applications;
  • Work on the full lifecycle of machine learning development, including training data collection, model training, component and end-to-end evaluation;
  • Design and get high quality labels for a wide range of techniques (retrieval, ranking, machine reading comprehension etc.);
  • Select, develop and build metrics to use across the full range of search engine components;
  • Incorporate customer feedback into evaluations and models development;
  • Contribute to experimentation infrastructure and proofs-of-concept to test out new ideas and concepts;
  • Understand existing code and write new code that is efficient and readable;
    Partner effectively with program management, engineers, and other functions across products;
  • Document work and communicate in a clear and efficient way.