Developing an AI project development life cycle involves five distinct tasks. No single individual has enough skills (or time) to carry out all tasks in AI project development. Thus, teams include individuals who focus on part of the cycle. Here is a visual representation of six technical roles and how they relate to various tasks.

I   What tasks does a deep learning researcher carry out?

Deep learning researchers carry out data engineering and modeling tasks as shown in Figure 1. This includes:

• data engineering subtasks such as defining data requirements, collecting, labeling, inspecting, cleaning, augmenting, and moving data.
• modeling subtasks such as training deep learning models, defining evaluation metrics, searching hyperparameters, and reading research papers.

Although it’s not represented in Figure 1, some deep learning researchers focus on deployment (for instance life-long learning, model memory, or optimization for edge deployment) and AI infrastructure (such as distributed training, scheduling, experiment, and resource management).

II   What skills does a deep learning researcher need?

Deep learning researchers demonstrate outstanding scientific skills (see Figure 2). Communication skills requirements vary among teams. They mostly write prototyping code, as opposed to production code written by engineers, and throw out most of the code they write.

Compared to machine learning researchers, this role requires deep learning knowledge in addition to the skills profile presented in Figure 1. It focuses on applications, usually powered by deep learning, such as speech recognition, natural language processing, and computer vision. Hence, it requires skills specific to deep learning projects such as understanding and using various neural network architectures such as fully connected networks, CNNs, and RNNs.

If you’re interested in comparing your skills to other deep learning researchers, we recommend taking the standardized machine learning, deep learning, data science, mathematics, and algorithmic coding tests on Workera. If you’re a company hiring deep learning researchers, you can administer computerized tests to AI job applicants for free using Workera Test and connect with AI practitioners using Workera Connect.

III   What tools does a deep learning researcher use?

Deep learning researchers in different companies use different tools, but some tools stand out. The following tools grouped by task are the most frequently used tools identified in our research.

• Data engineering in Python and/or SQL (or other domain-specific query languages).
• Modeling in Python using packages such as numpy, scikit-learn, TensorFlow, PyTorch, and the like.
• Collaboration and workflow using a version control system like Git, Subversion, and Mercurial, a command line interface (CLI) like Unix, an integrated development environment (IDE) such as Jupyter Notebook or Sublime, and an issue tracking product like JIRA.
• Research by following updates via channels such as Twitter, Reddit, Arxiv, and conferences such as NeurIPS, ICLR, ICML, CVPR, and ACM.

IV   In what team structure does a deep learning researcher fit?

Building an AI team requires bringing together complementary individuals who can progressively carry out the tasks of the AI project development lifecycle. AI teams focus on data engineering and modeling from the beginning, because they need to validate the feasibility of an AI project or idea. As the project becomes more mature, the team starts focusing on deployment, business analysis, and AI infrastructure.

Deep learning researchers achieve their fullest potential in a research environment, supported by teams in charge of deployment, business analyses and AI infrastructure. They combine well with data analysts who focus on translating statistics into actionable business insights and software engineers who build the tools and infrastructure that increases the effectiveness of all tasks.

Conclusion

This article aims to clarify what a deep learning researcher is, what tasks they carry out, and what skills they need. If you’re an AI practitioner, we hope it helps you choose a career track.

Companies may refer to this position as deep learning researcher, research scientist, research engineer, data scientist, and many other titles. If you’re a hiring manager, we hope that it helps you define your job requirements.

AI organizations are constantly evolving, so this article is a work in progress. We intend to revise it as our team learns more about new roles.