Software Engineer II Microsoft
Redmond, WA

Job Description

Ever dreamed of making bots have natural conversations? Do you happen to have experience successfully delivering AI/NLP experiences into production to make that more than a dream?

If you do, we may just have your dream job waiting! We in the Customer Care Intelligence team (formerly project Toronto) are currently building a SaaS product to easily enable companies to digitally transform their customer care through the power of AI. Through our Virtual Customer Service Agent and our Insights analytics product we will enable customer service teams to create and maintain high quality customer service bots to help customers through chat & voice; all without the need for AI experts, Data Scientists or programmers.

Here is Satya talking about our work: https://www.linkedin.com/feed/update/urn:li:activity:6447894933520158720, and here are more details: https://youtu.be/Pk-AVqQPUg8

Having created major bespoke customer service chat bots (where we have bots in production, handling millions of engaged conversations a month) we have learned that making content creation and maintenance easy for the subject matter experts, is key to create and maintain the high quality content that is crucial for great customer experiences. We couple intuitive dialog creation (and novel dynamic dialog approaches) with intuitive analytics to enable non data scientists to easily understand which topics are impactful candidates for automation, how topics are performing and even which support topics cannot be confidently differentiated by the AI model and what to do about it.

We're looking for Applied Science Engineer to join Customer Care Intelligence Applied Science team. Our charter is to research & develop leading natural language processing, machine learning and deep learning techniques to enable customer care digital transformation with deep customer support insights and natural virtual agent conversation solutions. We look for people who are passionate about leading cutting-edge technologies in natural language processing, machine learning and AI. Strong experience with production engineering will be a big plus.

* Bachelor or master's degree in computer science, Engineering or related field
* A strong background in Machine Learning and/or Natural Language Processing
* 3+ years of programming experience using C#, C++, Python or Java
* Experience with open source machine learning and NLP frameworks is a plus
* Prior working experiences with shipping SaaS products is a plus
* Prior working experiences with developing ML models while respecting strict user and organization privacy policies is a plus

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.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

* Working with product teams and partner teams to understand business problem, collect data and analyze data
* Defining appropriate metrics to measure model effectiveness, correlating with business success goals - Designing and developing model evaluation pipeline
* Creating, running and evaluating different experiments - Designing and developing new solutions using different technologies, such as deep learning, supervised/unsupervised ML, reinforcement learning, etc.
* Working closely with product team to integrate models into products to meet product serving scaling and quality criteria