Machine learning engineers possess the strong analytical skills of data scientists and the computer programming acumen of software engineers. Read on to learn more about how they put these skills to work.
Here’s what we’ll cover:
Machine learning engineers perform a valuable function within organizations—sitting at the intersection of computer science and data science, they combine the analytical and data wrangling skills of data scientists (think: specialized knowledge of deep learning and machine learning algorithms, artificial intelligence, statistics, data modeling and data structures, mathematics, and data pipelines) with the coding expertise of software engineers to write scalable programs.
One way to think of a machine learning engineer’s core responsibility is as both a demystifier of a data scientist’s findings and a person who can implement ML algorithms into software. Like data scientists, machine learning engineers have strong data management skills and can work with large data sets, perform statistical analysis, and have an understanding of a data scientist’s code. But where a data scientist is mainly concerned with generating valuable insights that can then be shared with stakeholders, a machine learning engineer’s end goal is to design self-running and self-learning software that can ingest data and offer increasingly accurate outputs.
For this reason, machine learning engineers are in high demand in many fields, from retail—where shopping platforms such as Amazon rely on automated systems to make purchase recommendations to customers; to entertainment—where streaming services like Netflix use machine learning algorithms to power their viewing recommendation systems; to automakers—where AI is the reason why a growing number of cars can drive themselves.
The specific demands of a machine learning engineer differ depending on organization, team, and domain, but a typical machine learning engineering job description will emphasize the need for someone who can design and train computers to learn automatically.
For example, Tesla's listing for a deep learning engineer in its Autopilot division seeks someone who can “research, design, implement, optimize, and deploy deep learning models that advance the state of the art in perception and control for autonomous driving,” and to use those skills to “train machine learning and deep learning models on a computing cluster to perform visual recognition tasks, such as segmentation and detection.” In other words, a machine learning engineer in this role would be tasked with teaching a car’s computer to learn on its own.
At Facebook, a job listing for a machine learning engineer requires that a successful candidate be able to “develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules-based modeling” for its augmented and virtual reality platforms. In this role, a machine learning engineer would teach Facebook’s AR and VR products to consume location data so that they can then power virtual and augmented experiences for consumers.
And at Amazon, a job listing for a machine learning engineer describes the role as working on a technical solution for sponsored ads to “ensure relevant ads are served to Amazon’s customers”—in other words, automating a data-driven program to help Amazon meet its business goals.
Across the board, machine learning engineer job descriptions also typically call for having a bachelor’s or advanced degree in computer science, mathematics, statistics, or a related discipline; proficiency with programming languages such as Python, Java, SQL, and C++; and experience with tools and programs such as TensorFlow, SciKit, Keras, PyTorch, Spark, and AWS.
While teaching software and systems to learn on their own without human intervention is the high-level goal of many machine learning engineers, there are many day-to-day tasks and responsibilities that an engineer needs to accomplish in order to achieve that goal.
For example, a few responsibilities that recur across different job listings include: running machine learning experiments using programming languages and machine learning libraries; deploying machine learning solutions into production; optimizing those solutions for performance and scalability; implementing custom machine learning code; performing foundational data science work such as analyzing data and coming up with use cases; and performing foundational data engineering work by ensuring a good flow between databases and backend systems.
Broken down even further, a machine learning engineer might consider how the needs of a customer can be met through machine learning and work backward from there by brainstorming with team members and stakeholders. Or they might work on improving the efficiency of production models by revisiting and fine-tuning existing models on a regular basis.
In addition to interpreting data and programming software, machine learning engineers are also typically responsible for researching the latest tools, programs, and algorithms that will help them complete the job. For example, in a Snapchat job description for a machine learning product engineer, the company expects a potential hire to “apply findings from cutting edge research to help solve challenging product problems.”
A job description for a machine learning engineer at Square similarly emphasizes the role of research, with responsibilities such as building relationships with partner teams, framing and structuring questions, collecting and analyzing data and performing research that will lead to stronger prototypes and machine learning pipelines.
Technical responsibilities aside, machine learning engineers are also responsible for being collaborative and communicative because they often work with data analysts, data engineers, and product managers. In this collaborative capacity, they are trusted with providing machine learning expertise, organizing and summarizing insights and results for business partners, and providing support to other data and engineering specialists on the team.
Is machine learning engineering the right career for you?
Knowing machine learning and deep learning concepts is important—but not enough to get you hired. According to hiring managers, most job seekers lack the engineering skills to perform the job. This is why more than 50% of Springboard's Machine Learning Career Track curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service. No other bootcamp does this.
Our machine learning training will teach you linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.
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