Senior Software Engineering Manager - Windows & Web Experiences

Why work at Windows and Web Experiences
Inside Microsoft's Web Experiences Team 

Crowdsourcing is critical for many of Microsoft’s machine learning features. The UHRS (Universal Human Relevance System) team within WWE (Windows and Web Experiences Team) is central to these efforts. We are responsible for the platform that enables data annotation using a large, diverse, worldwide set of labelers. This data is foundation to evaluate and train AI (Artificial Intelligence) and ML (Machine Learning) models across Microsoft. We are one of the largest AI labelling platforms worldwide and aspire to grow by a magnitude in the coming years. With the advent of AI and LLMs (Large Language Model), we have transformed our platform to increase the number of labels collected using various LLM models. The scaling of our platform should be increased by 20X, with improved analytics for labelers and task owners, as well as more control for task owners, all while making everything simpler to use. Additional work will involve creating entirely new serving architecture, task recommendation system for labelers, labeler reputation, various labeler and task analysis, labeler and task dashboards, alerting, etc.

We are looking for a proven technical leader that can join our growing team in Barcelona. You will find engineering challenges around big data and have the opportunity to work with world class data scientists. You will work across our stack, from our public-facing React-based UX, to our Azure-hosted backend, to analyzing data with the latest available tools. You will impact the latest products across Microsoft, used by billions of people.

Required Qualifications: 

• MS/BS in Computer or Data Science or other relevant fields or comparable work experience 
• Extensive experience with C#, Python, Java or any other major programming language  
• Ability to work independently, strong collaboration and communication skills 
• Strong software engineering background with experience in writing robust and maintainable production code.
• People management experience
• Experience in modern web development  preferred.
• Exceptional problem solving & critical thinking. 
• Strong communication skills and customer empathy.
• Passionate and self-motivated, with a demonstrated ability to work in a fast-paced environment. 
• Team-player that will put team and organization first.
• Interest in designing dashboards for data visualization in novel ways. 

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.

• Mentor software developers in their technical and professional development
• Champion data-driven decisions for feature prioritization, delivery and improving insights into data for our partners
• Collaborate with product team and other organizations within Microsoft to deliver success
• Defining labeler and task quality metrics and coming up with proper thresholds for actions
• Coming up with labeler skill validation and incentives for more work, bonuses, awards, etc.
• Defining gamification for labelers and coming up with recommendation systems to send tasks to labelers.
• Task routing e.g., sending basketball queries to labelers that like basketball.
• Providing insights on vast amount of data collected in the system and suggesting actions
• Optimizing the cost of label collection while maintaining/improving quality and throughput
• Defining the overall label quality and coming up with recommendations to improve it.