Building a Career Path in Machine Learning

At Illinois Tech, Kaylee Rosendahl (MATH ’24/M.S. CS ’25) found the research experience, coursework, and mentorship she needed to develop the skills, talents, and knowledge to launch a career as a machine learning engineer at 66degrees.

As a student research assistant, Rosendahl spent several years studying data filtering techniques with applied mathematics faculty in supervised machine learning and regression. That stemmed from participation in Illinois Tech’s Summer Undergraduate Research Experience program after her first year, where she found herself implementing a data-dimension reduction algorithm proposed in a faculty-written paper. It was her first experience working with code and academic research, and it became a quick lesson in how to solve a real-world problem and develop skills in programming and creating accompanying visuals and slide decks. It also paved the way for Rosendahl’s opportunity to write and run simulations for pLEO satellite systems at UCLA’s Institute of Pure & Applied Mathematics.

“There are so many different ways to go upward at Illinois Tech,” she says. “You can find research, form connections with faculty, start your own organization, or pursue multi-degree opportunities. They may have to be sought out, but they are there.”

She says that she found a mathematics course within Illinois Tech’s Interprofessional Projects (IPRO) Program,  Special Problems in Business, Government, and Industry, to closely mimic real-world situations students might encounter after graduation. The course emphasized the importance of data engineering, transformation, and modeling, which are the foundations of computing, mathematics, and data science fields. The success of the course led to Illinois Tech’s B.S. in Data Science program.

Rosendahl says that she found mentors among the applied mathematics faculty and that developing relationships with faculty was easy.  She says faculty advisers directed helpful initiatives, such as the Society for Industrial and Applied Mathematics (SIAM) Student Chapter, poster sessions, and community events. Associate Professor Hemanshu Kaul always encouraged her to pursue greater opportunities in their frequent conversations. Others sent program boards and websites to her inbox and wrote many letters of recommendation.

“I’m not sure where I would have ended up at if I didn’t have the support and push from the math faculty,” Rosendahl says. “Maybe the talent, skills, and knowledge were already there, but I had to learn to be proactive in my career. It is a great thing to know people will tend to want the best from you.”

Rosendahl says that understanding the underlying logic behind a model or an algorithm is a rare trait in her field, and the knowledge that she acquired from Illinois Tech has given her a competitive career advantage, especially in this age of reliance on artificial intelligence. It is through this type of knowledge that emerging technologies, as well as newer and faster models, can come into fruition.

Currently, Rosendahl is a machine learning engineer at 66degrees, a Google Cloud Platform-partnered consulting firm based in Chicago. Her day is spent designing, implementing, and testing a variety of ML systems, such as retrieval-augmented generation (RAG), ML Operations and production-ready continuous integration/continuous delivery (CI/CD), data science modeling, and agentic decision-making workflows. She often works alongside a data architect, and together they interact with clients to define problems or shortcomings in their business, identify platforms or tools that can be utilized, build out a solution, conduct extensive user testing, and hand off a solution with knowledge transfers and documentation.