Applied Scientist

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We are looking for an Applied Scientist to join our team! As an Applied Scientist for BIC Agent Cloud, 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, building your expertise in machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experiences. This role will combine AI knowledge with applied science expertise and demonstrate a growth mindset and customer empathy. Join us in shaping the future of AI agents.

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.

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 Computer Science, Statistics, Electrical/Computer Engineering, Physics, Mathematics or related field AND relevant internship experience (e.g., statistics, predictive analytics, research)
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
  • OR equivalent experience.
• Hands-on experience with generative AI OR LLM/ML algorithms.

Other Requirements:

• 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.

Applied Sciences IC2 - The typical base pay range for this role across the U.S. is USD $84,200 - $165,200 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 $109,000 - $180,400 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

Microsoft will accept applications for the role until 11/18/2025.

• Research, implement, and fine-tune state-of-the-art foundation models, leveraging techniques such as prompt engineering, RAG, multi-agent architectures, and classical ML to deliver business impact.
• Build benchmarks, datasets, and metrics to assess language model performance, addressing relevance, bias, hallucination, and response quality through offline and online experiments.
• Design rapid AI prototypes, contribute to production deployments, debug code, and support MLOps/AIOps for scalable and reliable AI systems.
• Convert cutting-edge AI research into production-ready solutions, measure impact via A/B testing and telemetry, and align innovations with strategic business goals.
• Apply fairness, bias mitigation, and privacy principles throughout the AI lifecycle, proactively addressing ethical and security risks such as XPIA attacks.
• Partner with product and engineering groups to integrate generative AI solutions, share insights on industry trends, and promote knowledge through documentation and internal forums.
• Prepare and analyze datasets, develop and evaluate ML models using modern frameworks, and address scalability and performance challenges in large-scale environments.