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The Informatics Skunkworks program provides a new framework for engaging undergraduates in research experiences, with a focus on the interface of data science and materials science. The program seeks to provide authentic research, engaged personal learning, and professional development while also being efficient, accessible, and scalable. Initially developed at the University of Wisconsin-Madison, participation continues to grow, with over 90 students engaged in research or training activities during the Fall 2021 semester from 4 institutions. The Skunkworks focuses on reducing barriers to engagement for mentors and students in undergraduate research by replacing bespoke and ad-hoc approaches with efforts and infrastructure that are reusable and scalable, including simplified standardized recruiting methods, online modular training resources, flexible undergraduate accessible software tools, long-term research projects with many similar but distinct components to engage large teams, and support from a learning community. For example, new students have the option to participate in a modular, self-paced, online onboarding curriculum that teaches students the basic skills needed for most data science projects, thereby dramatically reducing the mentor time needed to engage students with limited background in machine learning research. Projects are authentic research challenges that strive to allow for large flexible teams, thereby scaling up their impact from the typical engagement of just one or two students and allowing for extensive peer teaching. Throughout the program, professional development activities are efficiently delivered through standardized materials to teach critical research skills like record keeping, establishing group expectations and dynamics, and networking. These skills are also reinforced at workshop events hosted during the semester, which are effectively delivered online and yield growing impact for modest effort as the community grows. The program has been successfully implemented as evidenced by the last two semesters' evaluation findings through interviews, focus groups, and pre-post surveys. The students reported a positive attitude towards the program. Students' perception about machine learning knowledge and skills and their self-confidence improved after they got involved in the program. The instructors and mentors indicated positive teaching and mentoring experiences, and shared ideas on the further improvement of the program. Building on its early successes the team is continuing to implement evaluation data-driven improvements to the program with the goal of continuing to grow through new collaborations.