Senior AI Applied Scientist

Microsoft•Redmond, WA

About The Position

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. - 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. ⁻ Proven programming expertise (e.g., in Python or leveraging AI-first IDEs and SWE agents), with a strong record of building reliable, well-documented research code that drives rapid experimentation, scalable evaluation, and efficient deployment from prototype to production in applied AI research. 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. Documentation ⁻ Maintain clear documentation of experiments, results, and methodologies. ⁻ Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing Ethics, Privacy and Security 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.

Requirements

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.
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

Responsibilities

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.
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.
Proven programming expertise (e.g., in Python or leveraging AI-first IDEs and SWE agents), with a strong record of building reliable, well-documented research code that drives rapid experimentation, scalable evaluation, and efficient deployment from prototype to production in applied AI research.
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.
Maintain clear documentation of experiments, results, and methodologies.
Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing
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.