Senior Applied Scientist at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Feb, 26

Salary

0.0

Posted On

22 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI-Driven Impact, Foundation Models, Prompt Engineering, Graphs, Multi-Agent Architectures, Classical Machine Learning, MLOps, AIOps, Generative AI, Deep Learning, Fairness and Bias in AI, Data Analysis, Model Development, Production Deployment, Scalability, Performance Monitoring

Industry

Software Development

Description

Bringing the State of the Art to Products Build collaborative relationships with product and business groups to deliver AI-driven impact. Research and implement state-of-the-art using foundation models, prompt engineering, RAG, graphs, multi-agent architectures, as well as classical machine learning techniques. Build rapid AI solution prototypes, contribute to production deployment of these solutions, debug production code, support MLOps/AIOps. Contribute to papers, patents, and conference presentations. Translate research into production-ready solutions and measure their impact through A/B testing and telemetry that address customer needs. Ability to use data to identify gaps in AI quality, uncover insights and implement PoCs to show proof of concepts. Apply a deep understanding of fairness and bias in AI by proactively identifying and mitigating ethical and security risks—including XPIA (Cross-Prompt Injection Attack) unfairness, bias, and privacy concerns—to ensure equitable and responsible outcomes. Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring. Contribute to internal ethics and privacy policies and ensure responsible AI practice throughout AI development cycle from data collection to model development, deployment, and monitoring. Design, develop, and integrate generative AI solutions using foundation models and more. Deep understanding of small and large language models architecture, Deep learning, fine tuning techniques, multi-agent architectures, classical ML, and optimization techniques to adapt out-of-the-box solutions to particular business problems. Prepare and analyze data for machine learning, identifying optimal features and addressing data gaps. Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks and state-of-the-art models, open-source libraries, statistical tools, and rigorous metrics. Address scalability and performance issues using large-scale computing frameworks. Monitor model behavior, guide product monitoring and alerting, and adapt to changes in data streams. Bachelor's degree in Computer Science, Statistics, Electrical or Computer Engineering, Physics, Mathematics or related field AND 4+ years of experience in AI/ML, predictive analytics, or research OR Master's degree in Computer Science, Statistics, Electrical or Computer Engineering, Physics, Mathematics or related field AND 3+ years of experience in AI/ML, predictive analytics, or research OR Doctorate in Computer Science, Statistics, Electrical or Computer Engineering, Physics, Mathematics, or related field AND 1+ year(s) of experience in AI/ML, predictive analytics, or research OR equivalent experience. 1+ years of experience with generative AI or LLM/ML algorithms. Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines. Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow). 3+ years of experience publishing in peer-reviewed venues or filing patents. 3+ years of experience conducting research in academic or industry settings. 1+ year of experience developing and deploying live production systems. 1+ years of experience working with Generative AI models and ML stacks. Experience across the product lifecycle from ideation to shipping. Experience presenting at conferences or industry events.

Responsibilities

The Senior Applied Scientist will build collaborative relationships with product and business groups to deliver AI-driven impact and research state-of-the-art solutions. Responsibilities include developing and deploying AI prototypes, contributing to production deployment, and ensuring responsible AI practices throughout the development lifecycle.