Microsoft

Principal Applied Scientist - CoreAI

United States, Washington, Redmond
✓ Verified live on the employer's own system · added 133 days ago
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Senior · 6+ yrs exp

Requirements

Education: Bachelor's degree or related field

Experience: 6+ years

Skills & tools

Machine LearningData AnalysisSecurityDistributed SystemsProgrammingResearchManagement
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Full job description

In this role you'll develop machine learning techniques that push the boundaries of safety, alignment, and trustworthy AI. You'll collaborate across product, research, and engineering teams to design, build, and maintain responsible AI systems that make an impact at scale—from early-stage development and experimentation to production, monitoring, and continuous iteration.

Bachelor's Degree in Computer Science, Computational Linguistics, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Computer Science, Computational Linguistics or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) These requirements include but are not limited to the following specialized security screenings: Experience and familiarity with large language models (LLMs).

Experience with and a solid foundation in large distributed systems, algorithms, and software engineering principles. Master's or PhD in Computer Science, Machine Learning, Data Science, or a related field. 5+ years of experience in a research/ML engineering or an applied research scientist position, ideally with a focus on AI safety.

Have hands-on experience with deep learning and transformer-based models Excel at problem-solving and analytics, with a proactive approach to challenges Thrive in fast-moving environments where priorities shift and definitions evolve.

Enjoy taking ownership end-to-end and learning whatever is necessary to get results. Comfortable working independently while thriving in cross-team collaborations.

Understand methods for training and fine-tuning LLMs, including distillation, supervised fine-tuning, and policy optimization.

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