Senior AI Applied Scientist

As a Senior AI Applied Scientist for Dynamics 365 Apps, 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 (insert role/ external title) to join our teamThis role will combine AI knowledge with applied science expertise anddemonstrate a growth mindset and customer empathy. Join us in shaping the future of AI agents.

This role requires you to be onsite 3 days a week in Microsoft's offices in Redmond, WA.  

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 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.

• 1+ years of experience with generative AI OR LLM/ML algorithms

Other Requirements:

• Ability to meet Microsoft, customer and/or government security screening requirements arerequired 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.

Preferred Qualifications:

• Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines.

• Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow)

• Experience developing and deploying live production systemsone or more of the following: C#, Java, React/Angular, TypeScript.

• Experience with design and implementation of enterprise-scale services 

• 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+ years of experience working with Generative AI models and ML stacks

• Experience across the product lifecycle from ideation to shipping

Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay 

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

Microsoft will accept applications for the role until November 1, 2025.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

#BICJobs #BETJobs #CXAJobs #DVJobs #CXJobs #DYNAMICS365 #WEAREHIRING  #WEWANTTOTALKTOYOU!

Bringing the State of the Art to Products

1. 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.

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. 

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, identifyingoptimal 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.