Senior AI Applied Scientist at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Feb, 26

Salary

0.0

Posted On

19 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI, Machine Learning, Generative AI, Foundation Models, Prompt Engineering, MLOps, Data Analysis, Model Evaluation, Ethics in AI, Statistical Tools, Deep Learning, Multi-Agent Architectures, Classical ML, Optimization Techniques, Research, Production Deployment

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. Fine-tune foundation models using domain-specific datasets. - Evaluate model behavior on relevance, bias, hallucination, and response quality via offline evaluations, shadow experiments, online experiments, and ROI analysis. 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. 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 Bachelor'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 Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 1+ years of experience with generative AI OR LLM/ML algorithms These requirements include but are not limited to the following specialized security screenings: 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 Experience presenting at conferences or industry events 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

Responsibilities

The role involves building collaborative relationships with product and business groups to deliver AI-driven impact and implementing state-of-the-art AI solutions. Responsibilities include fine-tuning models, evaluating model behavior, and ensuring responsible AI practices throughout the development lifecycle.