As an Applied Scientist II for BIC (Business Industry Co-Pilot) Agent Cloud, you will play a pivotal role in advancing Microsoft's mission to empower every individual and organization on the planet to achieve more. You will contribute to the development and integration of cutting-edge AI technologies into Microsoft products and services, ensuring they are inclusive, ethical, and impactful.

You will collaborate across product, research and engineering teams to bring innovative solutions to life, applying your expertise in machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experiences.

AI Mission and Impact

We are in an era of unprecedented innovation and openness. As Microsoft continues to lead in AI, we are seeking individuals to help tackle some of the most exciting and meaningful challenges in the field. Our vision is to build a truly open architecture platform that enables users to summon tailored AI agents to drive real-world outcomes.

We are looking for an Applied Scientist II to join our team in shaping the future of AI agents.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Required Qualifications

• 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)

• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

• 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

• 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.

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

• 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.
• Embody our Culture and Values

Specialty Responsibilities

• 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.