AI-Native Software Engineering Researcher

Carnegie Mellon University•Pittsburgh, PA

5d•Onsite

About The Position

The future of software engineering is AI-native. Our team works to shape that future through pioneering research, practical engineering methods, and close collaboration with government, industry, and academia. At the AI-Native Software Engineering Directorate, we focus on how today's engineering teams can quickly exploit emerging technologies and harness AI capabilities to create and evolve software quickly without losing fundamental software qualities. To address this new world, we : study and guide industry and government teams’ use of AI in software engineering distill best practices and lessons from experiments and experience to shape AI-native software engineering pilot and transition improvements with organizations and measure their success create novel agents and agentic workflows to support software engineering apply AI to accelerate architecture-scale modernization help organizations establish AI-native software engineering practices guide organizations in evolving their software with architecture and AI-native approaches Our overarching goal is to create new workflows and tools that enable AI-paced delivery of software without loss of quality. We're seeking candidates with a demonstrated track record who are motivated to shape AI-native software engineering. As an AI-Native Software Engineering Researcher, you will focus on conducting research in application of AI to industry and government scale software engineering problems, applying your research in government and industry settings, and disseminating results to engineering teams and the broader research and software engineering community. You will help shape our AI-Native Software Engineering strategy through exploration of challenges such as: assessing the rapidly changing capabilities of AI models and coding agents establishing metrics that help organizations understand their use of AI in development and guiding them through improvements identifying gaps in AI workflows that result in unnecessarily poor outcomes (e.g., excessive technical debt, bad architecture decisions, or frequent rework loops) creating novel agents or agentic workflows creating or adapting practices and workflows to embed software engineering discipline studying the cost implications of AI and developing strategies for more efficient use of AI (e.g., context management, token management, and balancing use of AI with non-AI approaches) You will have the opportunity to work with world class software engineers, researchers, and data scientists working on national and global scale problems.

Requirements

Comprehensive knowledge of software engineering fundamentals
Ability to keep up with and adapt to rapidly changing technical advances
A track record of conducting research and applying scientific methods to solve difficult problems
Strong written and verbal communication skills; ability to convey complex technical ideas in a layperson’s terms
Demonstrated track record publishing in top tier peer-reviewed venues
Strong collaboration skills for working with colleagues and sponsors
Proficiency in writing funding proposals or pitching ideas for new research projects
A PhD with at least two (2) years of experience.
Subject to a background investigation and must be able to obtain and maintain a Department of War security clearance.
Willing to travel up to 25% of the time to locations outside of your home location. Travel sites include SEI offices in Washington, D.C., sponsor sites, and conferences.

Nice To Haves

Experience leading research projects in novel areas
Successful application of AI to improve research or software engineering progress
Experience working with software engineering teams on large industry or government projects
Experience working with leadership to plan, develop, and deliver an overall technical strategy

Responsibilities

Conduct research in application of AI to industry and government scale software engineering problems.
Apply research in government and industry settings.
Disseminate results to engineering teams and the broader research and software engineering community.
Assess the rapidly changing capabilities of AI models and coding agents.
Establish metrics that help organizations understand their use of AI in development and guiding them through improvements.
Identify gaps in AI workflows that result in unnecessarily poor outcomes (e.g., excessive technical debt, bad architecture decisions, or frequent rework loops).
Create novel agents or agentic workflows.
Create or adapt practices and workflows to embed software engineering discipline.
Study the cost implications of AI and develop strategies for more efficient use of AI (e.g., context management, token management, and balancing use of AI with non-AI approaches).
Guide and mentor junior team members.