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AI/ML Engineer Career Path
A one-stop overview of the AI/ML Engineer path: what the role actually involves, the skills that show up in real job descriptions, live openings you can apply to right now, and how to get your resume and interview prep in shape.
What a AI/ML Engineer does
- Builds and trains models for tasks like classification, recommendation, or NLP
- Cleans and prepares datasets, then evaluates models against real metrics
- Deploys models into production systems and monitors their performance over time
- Works closely with data engineers to keep training data pipelines reliable
Skills employers look for
Open AI/ML Engineer jobs
Senior (devops) Machine Learning Engineer
Servicenow
No description available.
Staff Machine Learning Engineer
Servicenow
No description available.
Machine Learning Engineer 4
Adobe
About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse,...
Principal Data Scientist - Safety
Roblox
Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created ...
Senior Machine Learning Engineer
Speechmatics
We are hiring a Senior Machine Learning Engineer to contribute to cutting-edge Research & Development. As innovators in speech technology, our mission is to Und...
Machine Learning Engineer
Scarlet
We pull medical technology from the future to solve human health. Scarlet is authorised to assess and certify medical devices. We combine clinical, technical an...
Companies hiring AI/ML Engineers
Get ready to apply
What it takes to start in AI and ML
AI and ML roles expect more than the ability to call a library. Employers look for maths and statistics fundamentals, solid programming and the ability to take a model from a notebook to something that runs reliably.
True fresher openings are fewer than in general software, and many people reach ML after a first role in software or data. Treat that as a normal route, not a detour.
Questions people ask
Python, basic statistics and linear algebra, and a core library for data and models. Then build end-to-end projects with real data.
Some research-heavy roles prefer one, but many applied roles hire on skills and projects. Check the listings for your target role.
Publish projects that solve a real problem, explain your choices and show how you evaluated results. A clear write-up counts for a lot.