Principal AI Applied Scientist at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

19 Feb, 26

Salary

0.0

Posted On

21 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI, Machine Learning, Deep Learning, Python, MLOps, Data Analysis, Model Evaluation, Bias Mitigation, Generative AI, Statistical Tools, Frameworks, Optimization Techniques, Production Deployment, Research, Ethics, Fairness

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, and 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, identify optimal features, and address 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 6+ 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 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 8+ years of experience leading large-scale AI systems and cross-org initiatives that shipped. 8+ years of experience in software engineering foundations and hands-on depth in Python plus deep-learning frameworks (PyTorch/ TensorFlow) and modern MLOps/tooling. These requirements include but are not limited to the following specialized security screenings: PhD in AI/ML or related field with top-venue publications and/or patents. Experience architecting and deploying LLMs/multimodal models and multi-agent systems in production at scale. Familiarity with Responsible AI frameworks and bias-mitigation techniques. Demonstrated ability to shape product strategy and drive organizational change.

Responsibilities

The Principal AI Applied Scientist will research and implement state-of-the-art AI solutions, collaborating with product and business groups to deliver impactful AI-driven products. Responsibilities include fine-tuning models, evaluating model behavior, and ensuring responsible AI practices throughout the development lifecycle.