Principal Applied Scientist
Microsoft
Principal Applied Scientist
Redmond, Washington, United States
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Overview
The CoreAI 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 technologies, across a rich set of scenarios and languages. As a technology 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 the most challenging problems by inventing new algorithms. To that end, we welcome a Principal Applied Scientist to drive state-of-the-art speech modeling technologies, which impact millions of users.
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.
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.
Qualifications
Required Qualifications:
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
- OR equivalent experience.
- 5+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
- 5+ year(s) experience developing and deploying live production systems, as part of a product team.
- 4+ years experience working with a modern, high performance machine learning frameworks, such as PyTorch, MLX, Tensorflow, etc.
Other Requirements:
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred Qualifications:
- Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
- 3+ years experience conducting research as part of a research program (in academic or industry settings).
Applied Sciences IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 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 $188,000 - $304,200 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay
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Responsibilities
Your responsibilities include driving training and inference improvement to advance state-of-the-art speech technologies for real world user scenarios. Specifically, you will
- Drive large scale production model training framework development to support latest technologies and improve throughput.
- Drive model inference optimization and system integration to reduce cost and latency.
- Collaborate with runtime engineering team and modeling team to improve agility and productivity.