Job Information
Microsoft Corporation Senior Applied Scientist, Copilot AI in Redmond, Washington
Microsoft’s Business Applications Group is reimagining Dynamics 365 and the Power Platform applications using Generative AI, Large Language Models (LLMs), and copilots. The Microsoft Power Platform (Power Automate, Power Apps, Microsoft Copilot Studio, Power BI) is a rocket ship fueled by organizations across the globe investing in low-code/no-code development to accelerate their digital transformation ambitions. We are part of the AI team within Business Applications Group responsible for bringing AI to Enterprise solutions at scale. We are looking to hire a Senior Applied Scientist, Copilot AI to join our team. As part of this role, you will focus on building AI features using LLMs for Copilots powering the next generation of Automation solutions for Microsoft's Power Automate.
Power Automate is transforming Process Automation by seamlessly integrating Digital and Robotic Process Automation, by leveraging Artificial Intelligence and Natural Language Processing for smart automation and by enabling every human on the planet to use a low-code, no-code approach to automate end-to-end scenarios seamlessly across APIs (Application programming interface) and UI (User interface).
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.
In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.
Responsibilities
AI is now undergoing a transformational change with the advent of Large Language Models (LLMs) and capable of making the next generation scenarios in Automation a reality. We are seeking a Senior Applied Scientist with expertise in working with LLMs and other State of the art models to design scalable high fidelity systems. As a Senior Applied Scientist, Copilot AI you will build and measure quality for systems that allow LLMs to reason over large amounts of data as well as leveraging lighter weight models for specific scenarios. We are looking for an individual who has experience working with product teams and partnering with them to deliver joint solutions. This can range from collaborating to build fine-tuned models to devising ways to construct LLM chains for specific product needs.
Embody our culture and values .
All models released in Microsoft products need to comply with Responsible AI (RAI) standards adhering to ethical principles, respect human values, and promote social good. You will be ensuring everything you ship helps copilots be responsible AI systems. These include detecting harmful behavior, ungrounded/hallucinated answers, or jailbreak attempts.
Qualifications
Required/Minimum 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.
3+ years experience writing production code as well as deploying and maintaining shipped models over time.
2+ years experience working on deep learning architectures specifically with Natural language-based models, vector databases and graph databases.
2+ Experience designing data generation pipelines, experimentation plans, and setup metrics.
Preferred Qualifications:
Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) O
OR equivalent experience.
Experience developing applications using prompt engineering, fine tuning, Open AI or Azure Open AI APIs.
Experience collaborating effectively across partner teams.
Experience in Deep learning architectures and implementation using Pytorch or TensorFlow.
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $112,000 - $218,400 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 $145,800 - $238,600 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 May 16, 2024.
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Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations (https://careers.microsoft.com/v2/global/en/accessibility.html) .