FAQs Deep Learning Jobs

Frequently asked questions

Do not be discouraged by the laundry list of requirements some companies like to put in their job postings. While a standard resume usually features a graduate degree in computer science with a focus on machine learning, there are many paths to a deep learning role.

There are, broadly speaking, technical deep learning roles can be subdivided into three distinct types which in practice often overlap technical roles in deep learning project, data scientists responsible for data preparation, software engineers building maintainable code base to implement deep learning models, devops engineers in charge of creating stable deployments for ML models at scale and finally theorists or researchers working at the cutting edge of deep learning devising and experimenting with new models to improve performance or bring deep learning to new domains. While researchers are often recruited from academia or private research facilities such as OpenAI, all other roles are open to a variety of people from technical backgrounds from software engineering over business intelligence and data science to system administrators. Malte Pietsch CTO of deepset mentioned that even the theorists in his team of NLP experts often have backgrounds in fields such as linguistics and even classical languages. Josh from Generally Intelligent places emphasis on the software engineering abilities of most applicants preferring to teach deep learning fundamentals on the job if necessary, because solid software engineering is a foundational skill required for robust and corrects models. Something that everyone we interviewed mentioned, however, is the importance of proven ability in the form of portfolio projects or open source contribution. Even the most stellar recommendation letter from your previous employer can not be scrutinised in the same way an open source project can.

This significantly levels the playing fields for applicants, since anyone with the time and motivation can try their hand at implementing a paper, coming up with their own problem to solve or simply start contributing to their favourite open source deep learning project. So we recommend that you get your hands dirty acquiring real world skills and connections regardless of your background and then leverage that experience to get the role you want.

While linkedin reported a marked drop in AI job openings as a result of the pandemic and ensuing economic uncertainty in mid 2020, there are now over 40.000 jobs posted on the platform for the US alone and the job market seems to have recovered well.

The trend of adoption of AI technologies, meanwhile, continues unabated. Gardner IT forecasts that over 50% of all enterprise applications will have some AI based component by 2023. Of course most of these will make use of third party solutions. That is the flip side of a maturing field, more and more state of the art solutions will become available as off the shelf solutions. However, companies will need to invest in the institutional knowledge to select and manage those solutions and the increased demand for specialty solutions will create the need for startups and companies to develop them. This means that there will be great opportunity to combine domain knowledge with deep learning skills and more ways to set yourself apart as an applicant.

Rather than a second AI winter, we are entering the deployment phase of deep learning technologies as more and more sectors of the economy are poised to take advantage and we see an ever increasing array of startups to disrupt those industries from biotechnology, medical imaging to smart assistants in service industries to oil and gas exploration.

While this of course is highly dependent on the type of job there are common characteristics that companies look for in a candidate. The minimum requirement for an engineering position is usually an undergraduate degree in computer science or a related field or comparable experience. There is already a large degree of freedom in those requirements, you meat those requirements if you have a four year bachelors in computer science, physics or an engineering field or you are self taught, but with five years of experience working in an engineering role.

But this is just determines on which pile your application lands and whether you will receive a second look, the most important aspect that we determined from our interviews and the thousands of listing that have been posted on this site over the past five year is that you can demonstrate your technical knowledge and engineering abilities. There are no barriers to entry to putting your work out there, whether these are hobby project or open source work, but you need to somehow show that you walk the walk, no amount of degrees will make up for this. This work does not necessarily have to be in the field of deep learning or machine learning, as long as it allows you demonstrate your engineering abilities.

Deep learning jobs are hand curated by a fellow engineer, all jobs are vetted for their deep learning content. We scout for the most exciting and promising companies in the field and lead in depth interviews with them to uncover what makes them a great place to work and what you as potential applicants can do to sharpen their profiles.

Dozens of companies told us what they look for in a candidate

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