Prathyusha Charagondla graduated from the School of Information with a Master’s in Information and Data Science in 2022. Prior to the I School, she completed a Bachelor of Science in Data Science and Cognitive Science from UC Berkeley. She is currently a senior machine learning engineer of GenAI/Firefly at Adobe.
What was your favorite thing about the I School?
My favorite part of the I School was (and still is) the diversity of the student community. Everyone came from different backgrounds and career paths, bringing unique perspectives that made every discussion richer. I learned so much from my peers, both during and after the program, and I love how open and willing everyone is to share knowledge and help each other succeed.
What was your favorite class?
My favorite class in MIDS was Computer Vision (DATASCI281). I took the class around the time it was announced, which was very lucky timing, as I was on my last elective before Capstone. The class was an amazing experience and gave me a strong foundation in image processing and pushed me to get better at optimizing data transformations. Funny enough, this class was one of the key classes that helped me build the foundations, concepts, and context I needed to learn and grow in my current role.
You currently work at Adobe as a Senior Machine Learning Engineer! Tell us more about this.
As a Senior Machine Learning Engineer at Adobe, I‘m working on Firefly Foundry — our enterprise generative AI platform where we create custom, brand-safe models for customers’ unique use cases. Our job is to enable enterprises to leverage their models confidently, knowing the outputs are commercially safe and aligned with their brand. My work includes designing and improving models, data pipelines, and evaluating and running experiments. I’ve also previously worked on the end-to-end data delivery that a majority of the text-to-image and text-to-video models were trained on. While my role is very technical, there is a strong emphasis on data privacy and using responsible AI practices, which was a mindset that the MIDS program helped me develop.
What compels you to work in the field of data science and machine learning?
I love this field because it’s constantly evolving—there’s always something new to learn, and that keeps me on my toes. What excites me most is that much of the work is non-deterministic, so there’s rarely a single “right” answer; a lot of it is about exploration, experimentation, and learning along the way. That combination of innovation and curiosity makes the field both challenging and incredibly rewarding.
How do you utilize the skills/lessons you learned at the I School in this position?
One of the biggest takeaways from my time at the I School was learning how to navigate ambiguity, as so much of what we do in machine learning involves unclear requirements or evolving goals, so the ability to break down complex problems into structured steps has been invaluable. Another takeaway was the communication skills I honed as part of the program, as being able to explain technical concepts in a way that resonates with different audiences is key. How to read research papers and extract practical insights has stuck with me; it helps me stay ahead in a fast-moving field and bring innovative ideas into real-world applications.
Can you share any thoughts on how your identity has shaped your path, contributing to challenges/opportunities and/or unique skills, strengths, or perspectives that you bring?
Curiosity, adaptability, and a drive to keep learning have shaped my career journey. After graduating from the MIDS program, I pivoted from Site Reliability Engineering to machine learning, aided by the guidance and advice of mentors I was fortunate to have met through Adobe (especially through the Women Executive Shadow Program (WESP), which connected women with senior leaders for mentorship) and in the I School. Those experiences not only helped me clarify my goals but also gave me a diverse perspective on leadership and how to problem-solve, which are strengths I bring to every challenge.
What advice would you give your past self as an I School student?
The advice I would give myself is to continue building relationships and take full advantage of the incredible network around you. Most of my opportunities and experiences over the past few years have been influenced by the connections I’ve made and the mentorship I’ve received, both during and after the program.
