What is the difference between a Machine Learning Engineer and Deep Learning Engineer and an AI developer? What would be their daily tasks at the office?
Job titles in IT are somewhat arbitrary, and not very consistent even within the same country or general industry. Job titles in software development, especially newer forms of software development such as mobile apps, even more so. The latest trends in Data Science, Machine Learning and "AI" are at the sharp end of that.
There are some common themes - e.g. an AI Researcher is likely to be expected to be up to date with scientific papers and might be expected to produce some (or summaries/applications of same within the work environment) whilst a Deep Learning Engineer might be expected to be familiar with the ins and outs of provisioning compute resources in Amazon (AWS) or Google (GCP), moving data around for training or inference, with enough coding experience to integrate a TensforFlow model with some already-existing web service. However, none of this is strictly separated and a job advertised as "Data Scientist" for a smaller company might be expected to do both those things and more.
All this means that your best bet is to not take much notice of the job title. Read the job specification carefully. Approach each company with questions about daily tasks, responsibilities and assumed technical skills. Expect in some cases that the companies will be unsure themselves of what the job entails and expecting you to take a mature role in defining your own working relationship with the rest of the company - the more senior and experienced the role being looked for, the more likely this is. Both sides - prospective employee and prospective employer - may have incorrect assumptions about what is realistic and possible.
TL;DR: It's a bit Wild West out there at the moment, and likely will remain so over the next decade. To reduce variance in your own experience, research job posts carefully and talk in detail to employers about expectations in advance.
Deep learning is widely regarded as a subset of machine learning. Here are some definitions.
- AI: any technique that enables computers (machine) to mimic human brain, such as threshold logic, conditional clause. decision tree
- Machine Learning: A subset of AI that included statistically based method that utilize computer (machine) to improve at task with specific data
- Deep Learning: the subset of machine learning composed of algorithms that permit software to get train (learn) to perform tasks by (deep) multi layer neural network
Daily task of the engineer could be
- Looking for data, such as picture, sound, sensor output, video
- Making dataset from the data including cleaning, labeling
- Reading and studying other engineers' Git repositories, ML competition article and winner blog, opensource manual
- Implementing appropriate existing ML algorithms
- Running machine learning tests and experiments with different model, dataset, hyper parameter
- Sharing founding from training using visualization of the score
- Reading (data) scientists' research papers
- Designing and developing machine learning or deep learning systems