Machine Learning Engineer II
Microsoft
Machine Learning Engineer II
Redmond, Washington, United States
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Overview
The Microsoft Copilot Studio Applied Science and Research team is looking for a Machine Learning Engineer II.
The Microsoft Copilot Studio Applied Science and Research organization is seeking a Machine Learning Engineer II to contribute to the development and integration of cutting-edge AI technologies into Microsoft Copilot Studio, ensuring they are inclusive, ethical, and impactful.
You will collaborate across product, design, research and engineering teams to bring innovative solutions to life, applying your expertise in machine learning, data science, and software engineering to solve complex problems. Your work will directly influence product quality and customer experiences.
This role will combine machine learning and AI knowledge with software engineering expertise, while demonstrating a growth mindset and customer empathy. Join us in shaping the future of AI agents.
You will play a crucial role in developing the Copilot Studio Applied Science and Research team’s direction in machine learning, Generative AI model fine-tuning, Agent creation and deployment, AI evaluation and scaling.
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.
Qualifications
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
- 5+ years experience developing and deploying AI/ML products or systems at multiple points in the product cycle from ideation to shipping
Other Requirements
- Ability to meet Microsoft, customer and/or government security screening requirements is 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 IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 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 $131,400 - $215,400 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
Single reqs: Microsoft will accept applications for the role until August 17, 2025.
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Responsibilities
- Build and maintain internal tools to streamline model fine-tuning and evaluation workflows and automate repetitive tasks within secure development environments.
- Prepare and analyze data for machine learning, identifying optimal features and addressing data gaps.
- Implement machine learning algorithms, large-scale model fine-tuning, especially with closed and open source LLMs, SLMs, multimodal or task-specific models to solve real-world customer problems and deliver measurable product and customer impact.
- Contribute to or enhance existing innovations by refining models and training techniques through iterative improvements.
- Develop frameworks to assess model performance, monitor model behavior, conduct systematic benchmarking, and address identified weaknesses while ensuring compliance with customer standards.
- Write production codes and debug complex distributed systems.
- Provide subject matter expertise in AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models, reinforcement learning) to help translate cutting-edge research into practical, real-world solutions that drive product innovation and business impact.
- Demonstrate an understanding of small and large language models (SLMs and LLMs) architecture and optimization techniques to adapt out-of-the-box solutions to particular business problems.