The Azure AI Speech Group brings together talents in the areas of signal processing, speech modeling, statistical modeling and Deep Learning to develop and deliver robust, natural and scalable speech recognition and translation, across a rich set of scenarios and languages. As a science team in the Speech Group, we industrialized deep learning speech technologies, and contributed key innovations to the speech community. We work on all kinds of end-to-end speech technologies, targeting on the most challenging problems by inventing new algorithms.
To that end, we are hiring a Principal Applied Science Manager who is passionate at innovating the state-of-the-art speech modeling technologies which impact millions of users.
In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Required/Minimum Qualifications:
Other Requirements
Additional or Preferred Qualifications:
Applied Sciences M6 - The typical base pay range for this role across the U.S. is USD $161,600 - $286,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $209,600 - $314,400 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
Microsoft will accept applications for the role until January 9, 2025
Responsibilities are to lead a team to develop novel speech algorithms to advance state-of-the-art speech technologies for real world user scenarios, especially in integrating speech with LLM for multimodal modeling.
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