Join the PowerPoint team as we deliver modern, intelligent, and collaborative experiences that will delight millions of PowerPoint 365 customers. Located in the heart of Silicon Valley in Mountain View, CA, we are looking for an experienced machine learning scientist who is excited to apply ML to real-world problems and build models that will go into production.

On our team, understanding the customer is just the beginning. We analyze customer data, understand their problems, build models to address these issues, and collaborate with partner teams to deploy these models as customer-facing features. A flexible problem-solving attitude and the ability to collaborate across disciplines are vital. You will be responsible for working within Microsoft’s industry-leading commitments to user privacy and ensuring that your models and features adhere to Microsoft’s Responsible AI policies to ensure fair and unbiased models for all.

The PowerPoint team boasts mature development and engineering systems. Our engineers benefit from rich telemetry, data-driven decisions, rapid experimentation, and building on a world-class platform. We also have a great management team with extensive experience to help you advance your career. Our products and portfolio are rapidly expanding, and we are excited to meet the challenges of scale, performance, efficiency, and reliability as we build the world's finest storytelling software.

We are looking for Principal Applied Scientist to join our team in Mountain View, CA.

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.

Responsibilities

• Build and deploy Machine Learning (ML) models, create data pipelines, and manage training and test datasets

• Develop and ship machine learning models and large language model applications to customers

• Collaborate with research teams to adopt the latest technologies

• Measure the impact of models and work with product teams to design AI-powered experiences

• Foster a healthy and inclusive team environment, provide technical guidance to other applied scientists, and act as a mentor

• Embody our Culture and Values

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

• 6+ years of experience in machine learning, deep learning, natural language processing, computer vision, and/or statistics

• 6+ years of experience in end-to-end development for building, shipping, and iterating on high-impact ML models

• 6+ years of experience in software development

Preferred Qualifications

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical OR Computer Engineering, OR related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)

• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical OR Computer Engineering, OR related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

• OR equivalent experience

• Proficiency in Python and familiarity with coding in multiple languages

• Proficiency in using ML libraries from sources such as Hugging Face

• Expertise in implementing, training, and debugging transformer-based ML models and other related tools and technologies for building and shipping large language models (LLM) applications

• Deep knowledge of algorithms, machine learning, and distributed/cloud computing systems (e.g., Spark, Hadoop, Azure.

• Published papers in reputed ML conferences are a plus.

• Contributions to open-source ML projects are a big plus.

• Ability to quickly ramp up on various domains within Machine Learning

• Experience shipping high-quality products at scale is a plus

Applied Sciences IC5 - The typical base pay range for this role across the U.S. is USD $137,600 - $267,000 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 $180,400 - $294,000 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

Microsoft will accept applications for the role until November 24, 2024.

Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations (https://careers.microsoft.com/v2/global/en/accessibility.html) .