Hey everyone, I’m a college student who’s been focusing on machine learning and data science skills. I thought I had a solid background until I started browsing internship listings.
Every single ML/DS position seems to demand everything under the sun: web development, database management, cloud platforms, containerization, plus all the usual ML stuff. It feels like they want someone who can single-handedly architect an entire tech stack.
I just want to work with algorithms and data analysis, not become a one-person engineering team.
I have basic knowledge of version control, software development lifecycle, and some cloud concepts, but my web development skills are pretty limited. This makes me wonder if I should pivot and focus more on software engineering fundamentals.
Two questions for the community:
Are these job requirements realistic, or am I looking at overly ambitious postings? Do ML/DS interns actually need full-stack expertise?
If I should learn more software engineering, what’s the best approach?
I want to skip the beginner tutorials since I already know programming basics. Has anyone here made a similar transition? What learning path worked for you?
honestly curious - are you looking at entry-level internships or more senior positions? because i’ve noticed some companies post the same requirements for all levels which makes no sense. what specific companies are you seeing this pattern at? maybe you’re searching in areas where ml teams are really small?
Most internship postings are wish lists, not hard requirements. Companies cast a wide net hoping to catch candidates with different skills, but they don’t expect interns to nail everything listed. I applied to positions where I met maybe 60% of the requirements and still got interviews. Many ML/DS roles do involve some engineering work, especially at smaller companies where you’ll deploy models or build simple dashboards. But they won’t expect deep web development skills from interns. Focus on your core ML skills while picking up basic deployment concepts like APIs and containerization. Want to add engineering skills efficiently? Learn Python web frameworks like Flask or FastAPI for model serving, basic SQL for data manipulation, and Docker fundamentals. These tools directly support ML workflows without needing full-stack knowledge. Most successful data scientists I know learned this stuff on the job, not beforehand.