Senior Applied Scientist

Overview

As a Senior Applied Scientist , you will play a critical role in advancing Outlook's Co-pilot efforts in the areas of Large Language Model (LLM), Prompt Engineering, Finetuning, Evaluation, Relevance and Responsible AI (RAI). This multifaceted role is responsible for developing an end-to-end infrastructure and measurement framework, fostering cross-functional collaboration, and leveraging data science and AI expertise to guide decision-making. The successful candidate will work with multiple large organizations and stakeholders to drive the evaluation of our LLM systems and associated components.

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:

Ph.D. or Master's in a relevant field (e.g., Data Science, Computer Science, Social science, HCI, etc.). Extensive experience in data science, machine learning, experimentation, and AI, with a strong track record of delivering impactful results.
Expertise of LLM in finetuning, reinforcement learning, evaluation techniques, implementing RAG techniques, agentic workflows and industry best practices.
Proven expertise in program management and leading cross-functional teams.
Solid development experience in Python.
Excellent analytical and problem-solving skills.
Exceptional communication and presentation abilities.
Solid understanding of responsible AI principles.
Ability to work in a fast-paced and dynamic environment.

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.

Responsibilities

Strategic Leadership: Develop and execute a comprehensive strategy for LLM evaluation, encompassing LLM quality, costs, model performance, model utility (user experience and prompt effectiveness), and responsible AI considerations, in alignment with company-wide efforts and informed by emerging research. Drive cost effective solutions such as SLMs, or finetuned models. Data Science Expertise: Utilize strong data science skills to design experimentation, analyze data, create OKRs, create measurement and metrics, and derive actionable insights to enhance LLM systems. Responsible for influencing product and user experience based on evaluation results. Model and Prompt Evaluation: Lead efforts to assess and improve the performance and effectiveness of language models and prompts, driving iterative enhancements, including synthetic and manufactured data creation. Program Management: Oversee and manage large-scale, cross-functional evaluation programs, ensuring alignment with organizational objectives and timelines. Develop and maintain a robust measurement framework to track and report on LLM performance and user impact. Drive engineering product roadmap to construct automated evaluation pipelines integrated into the product workflow. User Experience Enhancement: Collaborate with User Experience teams to evaluate and optimize user interactions with AI systems, enhancing user satisfaction. Responsible AI (RAI): Implement RAI and DSB principles and guidelines in AI systems, ensuring ethical and unbiased practices in model development and deployment. Contribute to the LLM research body: form partnerships and lead deep research initiatives in areas of LLM evaluation and user experience optimization that contribute to the scientific body and deepen the product team's understanding and expertise of user mental models of and alignment to LLM-powered experience. Cross-Functional Collaboration: Work with engineering, research, product, and other teams to ensure seamless integration of evaluation processes into the development lifecycle. Stakeholder Engagement: Communicate findings and recommendations to executive leadership, fostering a data-driven culture within the organization. Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.Industry leading healthcareEducational resourcesDiscounts on products and servicesSavings and investmentsMaternity and paternity leaveGenerous time awayGiving programsOpportunities to network and connect

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