Microsoft Senior Applied Scientist 

United States, Washington 

253483614

As a Senior Applied Scientist, you will be part of a multidisciplinary team that partners with a selected group of strategic partners to co-innovate and drive breakthroughs in deep science and AI. You will have the opportunity to work on state-of-the-art technologies, including generative AI, interpretable Machine Learning (ML) models and large-scale multi-modality model training and production. Furthermore, you will have the chance to engage with customers and own the problem end-to-end from ideation to production.

• Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

• OR Doctorate in 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 creating publications (e.g., patents, libraries, peer-reviewed academic papers).

• Experience presenting at conferences or other events in the outside research/industry community as an invited speaker.

• 3+ years experience conducting research as part of a research program (in academic or industry settings).

• 1+ year(s) experience developing and deploying live production systems, as part of a product team.

• 1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

• Prefence for PhD degrees in large scale ML model training and deployment.

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 September 12, 2024.

Bringing the State of the Art to Products

Independently works to create product impact. Identifies approach, and applies, improves, or creates a research-backed solution (e.g., novel, data driven, scalable, extendable) to positively impact a Microsoft product or service. Designs an approach to solve significant business problems shared by a team member. May publish research to promote receiving new intellectual property for product impact.

• Collaborate with a multidisciplinary team to develop cutting-edge AI solutions for various industries.
• Conduct research and development in generative AI, multi-modality models, training, and production.
• Collaborate with strategic partners to co-innovate and drive breakthroughs in deep science.
• Directly engage with customers to understand their needs and provide tailored AI solutions.
• Own the problem end-to-end, from ideation to production, ensuring successful implementation.

Leveraging Applied Research

Masters one or more subareas (e.g., Object Recognition, Text Classification) and gains expertise in a broad area of research (e.g., Machine Learning, Natural Language Processing, Computer Vision, Statistical Modeling, Data-Driven Insights. Understands the corresponding literature and applicable research techniques. Uses expertise to identify the right technique to use when examining a problem.

Performs documentation of work in progress, experimentation results, plans, etc. Documents scientific work to ensure process is captured. Creates informal documentation and may share findings to promote innovation within groups or with other groups.

Specialty Responsibilities

Leverages data analysis knowledge to clean, transform, analyze, integrate, and organize data to the level required by the analysis techniques selected. Develops useable datasets for modeling purposes. Scales the feature ideation and data preparation. Takes cleaned or raw data and adapts data for machine learning purposes. Uses understanding which features are important that come out of the model and identifies the optimal features. Identifies gaps in current datasets and drives onboarding of new datasets. Works with team to optimize signal system design. Mentors and coaches are less experienced members in data cleaning and analysis best practices. Identifies gaps in current datasets and drives onboarding of new datasets (e.g., bringing on third-party datasets). Attempts to fix bugs in data to inform developers how to improve the products. Ensures representative data to honor problem definition and ethics. *

• Embody our