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What is the difference between an ML engineer and an applied AI engineer?
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The short answer
A machine learning engineer builds, trains, deploys and maintains models, and is judged on whether the model performs reliably in production. An applied AI engineer builds product features on top of models, usually models somebody else trained or a third party provides, and is judged on whether the feature works well for users at acceptable latency and cost.
Both titles are used loosely, and plenty of people hold one while doing the work of the other. The distinction is still worth drawing, because hiring the wrong one is the most common cause of a stalled AI initiative.
| Machine learning engineer | Applied AI engineer | ||
|---|---|---|---|
| Owns | The model and its lifecycle | The product feature built on a model | |
| Typical work | Training, evaluation, deployment, retraining, monitoring | Integration, orchestration, prompt and retrieval design, latency and cost tuning | |
| Judged on | Model performance in production | User outcome and product reliability | |
| Adjacent skills | Data engineering, MLOps, statistics | Backend engineering, product sense, UX awareness |
Owns
- The model and its lifecycle
- Machine learning engineer
- The product feature built on a model
- Applied AI engineer
Typical work
- Training, evaluation, deployment, retraining, monitoring
- Machine learning engineer
- Integration, orchestration, prompt and retrieval design, latency and cost tuning
- Applied AI engineer
Judged on
- Model performance in production
- Machine learning engineer
- User outcome and product reliability
- Applied AI engineer
Adjacent skills
- Data engineering, MLOps, statistics
- Machine learning engineer
- Backend engineering, product sense, UX awareness
- Applied AI engineer
Which one does your initiative need?
If your competitive advantage depends on a model trained on your own data, you need machine learning engineering. If your advantage is the product experience and the models are largely bought in, you need applied AI engineering, and hiring a research-leaning modeller instead will produce sophisticated work that nobody asked for.
Many scale-ups need the second before the first, and discover the need for the first only once the bought-in model stops being good enough for their specific problem.
Where the two overlap
In smaller teams one person often does both, and that person is genuinely valuable. The risk is assuming it from the title. Ask which half of the work they have done most recently and which half they want to do next, because the answer to the second question predicts retention better than the answer to the first.
Explored in depth
This topic is explored in more depth within How do you hire AI and machine learning engineers?.
See this in practice
Move from the concept to the way Saiyō delivers it.
Related questions
What should you assess in a machine learning engineer?
Assess four things: what the candidate has personally built and put into production, how they evaluate whether a model is working, how they handle data quality and failure, and whether their judgement fits the stage your initiative is at. Everything else, including the job title and the framework list, is a poor predictor.
Read the answerAnswerWhy do AI engineers not respond to job adverts?
Experienced AI engineers do not respond to job adverts because they are almost never looking: they are employed on interesting work, well paid, holding unvested equity, and receiving approaches every week already. An advert asks them to enter a process and compete for something they have not decided they want, which is a poor offer compared with a specific conversation about a specific problem.
Read the answer