Senior AI-Native Software Engineering Researcher

Carnegie Mellon University•Pittsburgh, PA

19h

About The Position

The AI-Native Software Engineering Directorate conducts research and develops practical engineering methods to help government and industry teams use emerging technologies and AI to create and evolve software quickly while maintaining fundamental software qualities. Its work includes studying and guiding AI use in software engineering, distilling lessons from experiments and experience, improving and measuring pilots and transitions, creating agents and agentic workflows, accelerating architecture-scale modernization, and helping organizations establish AI-native practices and evolve software using architecture and AI-native approaches. The overarching goal is to enable AI-paced software delivery without loss of quality. The Senior AI-Native Software Engineering Researcher conducts research on applying AI to industry- and government-scale software engineering problems, applies research in government and industry settings, and disseminates results to engineering teams and the broader research and software engineering communities. The researcher helps shape the AI-Native Software Engineering strategy by assessing AI models and coding agents; establishing metrics and helping organizations improve their use of AI in development; identifying workflow gaps that lead to technical debt, poor architecture decisions, or rework; creating agents and agentic workflows; embedding software engineering discipline in practices and workflows; and studying AI costs and more efficient use, including context and token management and balancing AI with non-AI approaches. The role involves working with software engineers, researchers, and data scientists on national- and global-scale problems. The position is based in Pittsburgh, Pennsylvania, and requires travel of up to 25% to locations outside the employee’s home location, including SEI offices in Washington, D.C., sponsor sites, and conferences. The position is full-time, regular staff employment. A background investigation is required, and the candidate must be able to obtain and maintain a Department of War security clearance.

Requirements

PhD with at least five years of relevant experience.
Comprehensive knowledge of software engineering fundamentals.
Ability to keep up with and adapt to rapidly changing technical advances.
Track record of conducting research and applying scientific methods to solve difficult problems.
Strong written and verbal communication skills, including the ability to explain complex technical ideas in 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.
Willingness and ability to guide and mentor junior team members.
Must undergo a background investigation and be able to obtain and maintain a Department of War security clearance.
Willingness to travel up to 25% of the time to locations outside the home location.

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 on applying AI to industry- and government-scale software engineering problems.
Apply research in government and industry settings.
Disseminate research results to engineering teams and the broader research and software engineering communities.
Help shape the AI-Native Software Engineering strategy by assessing changing AI model and coding-agent capabilities.
Establish metrics to help organizations understand and improve their use of AI in development.
Identify gaps in AI workflows that contribute to technical debt, poor architecture decisions, or frequent rework.
Create novel agents and agentic workflows.
Create or adapt practices and workflows that embed software engineering discipline.
Study the cost implications of AI and develop strategies for more efficient use, including context management, token management, and balancing AI with non-AI approaches.
Collaborate with software engineers, researchers, and data scientists on national- and global-scale problems.
Guide and mentor junior team members.
Travel up to 25% to SEI offices, sponsor sites, and conferences outside the home location.