Applied Scientist II 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

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

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

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 (Retrival Augmented Generation), 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 (Return on Investment) 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. 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. Leveraging Research in real-world problems Demonstrate deep expertise in AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models) to translate cutting-edge research into practical, real-world solutions that drive product innovation and business impact. Share insights on industry trends and applied technologies with engineering and product teams. Formulate strategic plans that integrate state-of-the-art research to meet business goals. 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 Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience. 1+ years' of hands on experience with generative AI OR LLM (Large Language Models)/ML (Machine Learning) Experience with MLOps Workflows, including CI/CD (Continuous Integration and Continuous Delivery/Deployment), monitoring, and retraining pipelines. Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow) A track record of publishing in peer-reviewed venues or filing patents Experience presenting at conferences or industry events A track record of conducting research in academic or industry settings Hands-on experience developing and deploying live production systems Experience across the product lifecycle from ideation to shipping

Responsibilities

The Applied Scientist II will research and implement state-of-the-art AI solutions, collaborating with product and business groups to deliver impactful results. Responsibilities include fine-tuning models, evaluating performance, and contributing to production deployment while ensuring responsible AI practices.