Microsoft
Applied Scientist II role at Microsoft for BIC (Business Industry Co-Pilot) Agent Cloud. The role involves developing and integrating cutting-edge AI technologies into Microsoft products and services, ensuring inclusivity, ethics, and impact. You will collaborate with product, research, and engineering teams to apply machine learning, data science, and AI to solve complex problems, influencing product direction and customer experiences.
Responsibilities
Build collaborative relationships with product and business groups to deliver AI-driven impact Research and implement state-of-the-art methods using foundation models, prompt engineering, Retrieval Augmented Generation (RAG), graphs, multi-agent architectures, and 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 impact through A/B testing and telemetry that address customer needs Demonstrate 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, multi-modal models) to translate cutting-edge research into practical 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 understanding of fairness and bias in AI by proactively identifying and mitigating ethical and security risks 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 to ensure responsible AI practice throughout the AI development cycle Embody Microsoft culture and values Specialty Responsibilities
Design, develop, and integrate generative AI solutions using foundation models Deep understanding of small and large language models architectures, deep learning, fine-tuning techniques, multi-agent architectures, classical ML, and optimization to adapt out-of-the-box solutions to 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 using modern frameworks, 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 Qualifications
Required Qualifications
Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (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 (statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience 1+ years’ hands-on experience with generative AI or LLM/ML Other Requirements
Ability to meet Microsoft, customer and/or government security screening requirements Microsoft Cloud Background Check: must pass upon hire/transfer and every two years thereafter Preferred Qualifications
Experience with MLOps workflows, including CI/CD, 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 Hands-on experience developing and deploying live production systems Experience across the product lifecycle from ideation to shipping Microsoft is an equal opportunity employer. Referrals and location-specific pay ranges are provided for informational purposes within the original posting. Applications are accepted until October 29, 2025.
#J-18808-Ljbffr
Build collaborative relationships with product and business groups to deliver AI-driven impact Research and implement state-of-the-art methods using foundation models, prompt engineering, Retrieval Augmented Generation (RAG), graphs, multi-agent architectures, and 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 impact through A/B testing and telemetry that address customer needs Demonstrate 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, multi-modal models) to translate cutting-edge research into practical 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 understanding of fairness and bias in AI by proactively identifying and mitigating ethical and security risks 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 to ensure responsible AI practice throughout the AI development cycle Embody Microsoft culture and values Specialty Responsibilities
Design, develop, and integrate generative AI solutions using foundation models Deep understanding of small and large language models architectures, deep learning, fine-tuning techniques, multi-agent architectures, classical ML, and optimization to adapt out-of-the-box solutions to 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 using modern frameworks, 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 Qualifications
Required Qualifications
Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (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 (statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience 1+ years’ hands-on experience with generative AI or LLM/ML Other Requirements
Ability to meet Microsoft, customer and/or government security screening requirements Microsoft Cloud Background Check: must pass upon hire/transfer and every two years thereafter Preferred Qualifications
Experience with MLOps workflows, including CI/CD, 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 Hands-on experience developing and deploying live production systems Experience across the product lifecycle from ideation to shipping Microsoft is an equal opportunity employer. Referrals and location-specific pay ranges are provided for informational purposes within the original posting. Applications are accepted until October 29, 2025.
#J-18808-Ljbffr