AIML - Staff Machine Learning Engineer - Answers, Knowledge & Intelligence (AKI)

1 Month ago • All levels • Research Development • $175,800 PA - $312,200 PA

Job Summary

Job Description

As a Staff Machine Learning Engineer, you will be involved in developing innovative data generation methods for personalized model and search system evaluations. You will also focus on creating online and offline metrics for quality assessment, and building scalable systems for comprehensive evaluation of the search experiences. Your responsibilities include designing and implementing data generation and data quality assessment methods, building scalable and automated evaluation systems, and designing and implementing offline and online metrics. You will collaborate with partner teams to define requirements, explore enhancements, and drive solutions for Personal Q&A quality. The role requires you to develop a long-term technical vision and contribute to the roadmap for Personal Q&A.
Must have:
  • Design and implement data generation methods for model evaluation.
  • Build scalable, automated systems for large-scale evaluation.
  • Design and implement metrics for assessing product quality.
  • Collaborate with teams on data requirements and enhancements.

Job Details

In this role you will have the opportunity to develop data generation methodologies for personalized model and search systems evaluations, online and offline metrics for quality assessment and build scalable systems for comprehensive evaluation. Our team is responsible for models and evaluation of search experiences that answer user’s questions using their personal documents with privacy at the forefront. Role responsibilities include: - Design and implement novel data generation and data quality assessment methods to support modeling and evaluation of personal Q&A. - Build scalable, automated systems for large scale, end-to-end evaluation of models and search powered systems. - Design and implement offline and online metrics to assess product and component level quality for personal Q&A. - Collaborate with partner teams to define data and evaluation requirements and priorities, and to explore opportunities for enhancements to the Personal Q&A stack. - Develop long-term technical vision for Personal Q&A quality; identify problem areas and drive solutions as part of a larger roadmap.

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