AI and machine learning hiring is busy, but the titles are confusing. The same work can be called “ML engineer”, “AI engineer” or “applied scientist”, and very different work can share one title. Knowing the difference saves you from applying to the wrong half of the market.
What the titles usually mean
- Machine learning engineer: builds, trains, deploys and monitors models in production. Strong software engineering plus ML.
- AI engineer: often builds products on top of existing models (LLMs): prompts, retrieval, evaluation, agents. Closer to product engineering.
- Applied scientist / research scientist: designs new methods and runs experiments. Research roles often ask for a PhD or published papers.
- Specialist roles: NLP, computer vision, deep learning, recommendation systems.
Read the responsibilities, not just the title. Jobly’s AI/ML role covers all of these, so add keywords such as “LLM”, “computer vision” or “MLOps” to narrow it.
Where AI roles are posted
- AI companies and labs usually hire through their own careers pages on Greenhouse, Ashby or Lever.
- Every other tech company is adding AI roles too, often inside existing product teams, and often titled “software engineer, AI”.
- Research roles also appear through universities, conferences and researchers’ own posts.
Show the right evidence
- For ML engineering: systems you put into production, with scale, latency or cost numbers.
- For AI engineering: products you shipped on top of models, and how you measured quality (evaluations, error rates, user feedback).
- For research: papers, code and results others can check.
- A public project on GitHub with a clear README often gets more attention than a list of courses.
Watch for hype
Some listings add “AI” to ordinary roles. That’s fine if you want the role, but check the day-to-day work before you tailor your application around models.
Stay early
Popular AI roles fill fast. Set Jobly to AI/ML with your experience level, and check new matches daily.
Common questions
Do I need a PhD?
For research scientist roles, often yes. For ML engineering and AI engineering, usually not: production experience and shipped work matter more.
I’m a software engineer. How do I move into AI?
AI engineering is the most direct path. Build something real on top of a model, measure how good it is, and write it up. Many teams also hire software engineers into AI product teams and teach the rest on the job.
Which keywords should I add in Jobly?
Use the words from roles you’d actually take, such as “LLM”, “MLOps”, “computer vision”, “recommendation” or “applied scientist”, and exclude the ones you don’t want, such as “research” if you prefer product work.