AI Applied Scientist
Microsoft
AI Applied Scientist
Redmond, Washington, United States
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Overview
You will collaborate across product, research and engineering teams to bring innovative solutions to life, applying your expertise in machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experience.
Microsoft is driving innovation and openness in AI, aiming to build an open architecture platform where users can deploy customized AI agents for real-world impact. The role seeks individuals with both AI and applied science expertise, a growth mindset, and customer empathy to help address significant challenges and shape the future of AI solutions.
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.
Qualifications
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND relevant internship experience (e.g., statistics, predictive analytics, research)
- OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
- OR equivalent experience.
- 1+ year(s) Experience with generative AI OR LLM/ML algorithms.
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.
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ 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
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
- OR equivalent experience.
- Familiarity with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines.
- Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow).
Applied Sciences IC2 - The typical base pay range for this role across the U.S. is USD $84,200 - $165,200 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 $109,000 - $180,400 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 6, 2025.
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Responsibilities
- Collaborate with product and business teams to deliver impactful AI solutions using advanced techniques like foundation models, prompt engineering, and multi-solution architectures.
- Fine-tune AI models with domain-specific data, evaluate and monitor their performance, and rapidly prototype and deploy AI systems.
- Support MLOps by translating research into production-ready applications, maintaining clear documentation, and sharing insights.
- Proactively address ethical, privacy, and security risks to ensure responsible AI development throughout the lifecycle.
- Design, develop, and integrate generative AI solutions using deep understanding of language models, deep learning, and optimization techniques to solve business problems.
- Prepare and analyze data for machine learning, identify optimal features, and address data gaps using modern frameworks and state-of-the-art models.
- Ensure scalability and performance of AI solutions, continuously monitor model behavior, and adapt to evolving data streams.