AI Roles Explained: What People Actually Do
Understand the main jobs in AI, their day to day work and skills, so you can choose where to aim
A taste of a lesson
What's the actual difference between a data scientist and a machine learning engineer?
Roughly: a data scientist mainly answers questions with data, analysing, experimenting and explaining findings to decision makers, so statistics and communication matter a lot. A machine learning engineer mainly builds and runs models in real products: code quality, pipelines, deployment and monitoring. The overlap is big and companies use the titles loosely, so always read the duties. Quick check: which sounds more like you, explaining why sales dropped, or making a recommendation system run reliably every day?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Describe the main AI and data roles through their everyday tasks
- Compare the skills and typical routes into each role
- Identify roles that match your interests and existing strengths
- Read job titles critically, knowing duties vary between organisations
Lesson plan
- 1 Why titles mislead Learn to focus on duties and daily tasks rather than job titles. Start
- 2 Building and running models Understand ML engineers, MLOps engineers and data engineers through their daily work. Start
- 3 Analysis and research Understand data scientists and research scientists and how they differ. Start
- 4 Building AI products Understand AI application engineers, product managers and designers. Start
- 5 Safety, policy and data roles Explore trust and safety, governance, evaluation and data specialist roles. Start
- 6 Finding your fit Match roles to your interests and strengths and plan a next step. Start
Try asking
About this tutor
For anyone curious about working in AI: students, career changers, parents advising teenagers, and people whose current job is changing. We walk through the main roles (machine learning engineer, data scientist, data engineer, AI or LLM application engineer, research scientist, MLOps engineer, AI product manager, designer, evaluation and data specialist, trust and safety, policy and governance) and what each does on a normal day, the skills involved, typical routes in and common misconceptions. No promises about salaries or jobs: just a clear map so you can make your own decisions.
Reviews
4.5
4 ratingsSample
- Ola N.Sample
Clear and down to earth. I'd love more detail on data annotation work, which is where I'm starting.
- Daniela F.Sample
Finally understand what all these titles mean. The day in the life walkthroughs helped me realise I want data engineering, not data science.
- Ruby T.Sample
Great overview, free too. The safety and policy roles section opened my eyes to options I didn't know existed.
- Kareem S.Sample
I used this to advise my daughter who's choosing a degree. Honest about PhDs and about how titles vary.
About the teacher
Career coach for people finding their place in AI work, technical or not
9 tutors 357 lessons taught Sample
I moved from marketing into data analytics and then into product work on AI features, so I know the confusion of career changes from the inside. Now I coach people who want to understand AI roles, move into them from other fields, or stay useful as their current job changes. I am direct about what roles involve day to day,...
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