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 machine learning engineer carry out?
Machine learning engineers carry out data engineering, modeling, and deployment 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 machine learning models, defining evaluation metrics, searching hyperparameters, and reading research papers.
- deployment subtasks such as converting prototyped code into production code, setting up a cloud environment to deploy the model, or improving response times and saving bandwidth.
Their skills complement those of people who analyze business metrics and build software infrastructure.
II What skills does a machine learning engineer need?
Machine learning engineers demonstrate solid scientific and engineering skills (see Figure 2). Communication skills requirements vary among teams.
They write both prototyping and production code, as opposed to data scientists who mostly write prototyping code and software engineers who write lots of production code.
If you’re interested in comparing your skills to other machine learning engineers, we recommend taking the standardized machine learning, data science, mathematics, algorithmic coding, and software engineering tests on Workera. If you’re a company hiring machine learning engineers, 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 machine learning engineer use?
Machine learning engineers 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.
- Modeling is primarily done in Python using packages such as numpy, scikit-learn, pandas, matplotlib, TensorFlow, and PyTorch.
- Data engineering happens in Python and/or SQL or other domain-specific query languages.
- Deployment is implemented using an object-oriented programming language (such as Python, and Java, C++,) and cloud technologies such as AWS, GCP, and Azure.
- Collaboration and workflow is managed with a version control system (for instance, Git, Subversion, and Mercurial), a command line interface (CLI) like Unix, an integrated development environment (IDE) such as Jupyter Notebook, and Sublime, and an issue tracking product like JIRA.
IV In what team structure does a machine learning engineer 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.
Machine learning engineers are versatile individuals. Their blend of engineering and scientific skills allow them to carry out most tasks of an AI project lifecycle. 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.
This article aims to clarify what a machine learning engineer 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 role as machine learning engineer, software engineer-machine learning, software engineer, data scientist, algorithm engineer, research scientist, research engineer, full-stack data scientist, and many more 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.