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Machine Learning Engineer

BrainStation’s Machine Learning Engineer career guide is intended to help you take the first steps toward a lucrative career in machine learning. Read on for an overview of the machine learning skills you should learn, career paths in machine learning, how to become a Machine Learning Engineer, and more.

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Machine Learning Engineer

A Machine Learning Engineer (ML engineer) uses big data tools and programming frameworks to build artificial intelligence systems that turn raw data into scaleable data science models. Sitting at the intersection of software engineering and data science, a Machine Learning Engineer develops algorithms, creates production-level data science models, and builds programs that control computer systems.

Machine Learning Engineers are data science professionals whose algorithms allow a computer or machine to identify patterns and gradually learn to understand commands. A subset of artificial intelligence, machine

Aside from training and retraining ML systems and models, it’s also the job of an ML Engineer to convert data science prototypes, create data visualizations to highlight insights and spot issues with data distribution that could affect real-world performance.

Click here to find out more about the Machine Learning Engineer role.

Data Science Vs Machine Learning

  • Data Science refers to a scientific approach to pulling actionable business insights from structured and unstructured data.
  • Machine learning refers to techniques Data Scientists teach computers to learn from data without explicit programming.

What Does a Machine Learning Engineer do?

Although the job responsibilities of a Machine Learning Engineer will depend on the role, industry, companies, or team they work for, ML Engineers generally spend their time designing, building, and working on machine learning systems.

In a sense, the job of a machine learning engineer isn’t all that different from that of a Data Scientist. Both job roles require you have top-notch data management and code skills and understand how to use data sets to train a model. But where a Data Scientist produces insights to present to business leaders, Machine Learning Engineers create software to automate predictive models.

Still, machine learning engineering is closely tied to data science and artificial intelligence, and your typical ML Engineer will likely work in close collaboration with a Data Scientist and Data Engineer.

Types of Machine Learning

There are several main basic types of machine learning or ML paradigms:

Supervised learning (SL)

This refers to the type of machine learning where labeled input-output pairs are used to train a system to classify data or make accurate outcome predictions. The most popular paradigm for machine learning, SL involves feeding input data into an algorithm or model, which adjusts its weights until the model has been fitted appropriately. Some common applications for this type of machine learning include email spam filters, face recognition tech, and Internet ad placement.

Unsupervised learning

The opposite of SL, unsupervised learning involves combing through unlabeled data to discover patterns that ultimately solve association or clustering problems. Algorithms are fed large amounts of data and equipped with the tools to understand the data. It then learns to cluster or organize that data in such a way that a human could understand it. You might see unsupervised learning in the recommendation systems on a streaming service or targeted ads based on buying habits or browsing data.

Reinforcement learning

Reinforcement Learning is the training of machine learning models to make a series of decisions. Where supervised and unsupervised learning are associated based on the presence or lack of labels, in reinforcement learning, artificial intelligence is rewarded or punished based on its actions. The AI gradually learns through trial and error how to solve a problem, usually beginning with random trials before finding more advanced and sophisticated tactics after learning the parameters of the game. Examples of reinforcement learning applications include in industrial simulations as well as video games.

Benefits of Machine Learning

Machine Learning is a buzzy term in the business world right now for good reason. There are many real-world benefits to ML, including:

  • Quickly spot trends and patterns. Deploying machine learning techniques allows companies to spot meaningful trends in data that humans wouldn’t detect. Ecommerce sites and streaming services can use ML to quickly comb through user data and produce products and deals that will be relevant.
  • Improves security. Anti-virus software and email spam filters are two of the ways in which machine learning keeps our computers and profiles more secure.
  • Engage and retain customers. Machine learning helps companies quickly process data about their customers and clients, which in turn allows for deep personalization options that improve user experience and keeps customers coming back.
  • Handle unwieldy data. Machine learning algorithms are a good option for dealing with multi-dimensional data in a dynamic environment.

Machine Learning Engineer Salaries

Machine Learning Engineers make an average salary of $151,622 in the United States according to Indeed. That average salary can soar as high as $200,000 in the Bay Area. Salaries also increase when you consider that most machine learning engineer jobs include perks like stock options and other financial incentives and bonuses.

Demand for Machine Learning Engineers

Machine Learning Engineers are in high demand, with the number of Machine Learning Engineers increasing by 344 percent from 2015 to 2018. High demand (as well as high salaries) was one of the motivating factors behind Indeed listing ML Engineer as the best job in the U.S. (along with related roles Data Scientist and Computer Vision Engineer).

What Skills do Machine Learning Engineers Need?

To succeed as a Machine Learning Engineer, you must combine the knowledge and skill sets of a Software Engineer and a Data Scientist. That means understanding all the fundamental concepts of computer science and data analysis, while also possessing some soft skills necessary to both disciplines.

Data Science skills

A Machine Learning Engineer is expected to have many of the same competencies as a Data Scientist, including data modeling, technical proficiency with programming languages like Python and Java, and understanding how to evaluate predictive algorithms and models. An understanding of probability and statistics would be very helpful as well.

Software Engineer skills

Some of the key computer science concepts important for ML Engineers are algorithms (and knowing how to write algorithms that can sort, optimize and search), understanding data structures, and having knowledge of computer architecture. Since an ML Engineer’s typical output is software, they should also understand how to follow software engineering best practices, especially those relating to system design, version control, testing, and requirements analysis.

Machine Learning Engineer skills

Though a Machine Learning Engineer is often thought to sit at the intersection between data science and software engineering, there are still some competencies uniquely important to ML jobs. Many Machine Learning Engineers are now training in deep learning, neural network architectures, natural language processing, and dynamic programming.

Soft skills for Machine Learning Engineers

Even though machine learning is a technical job title, soft skills are important too. Even if you possess top-notch knowledge of machine learning, you will also require polished skills in communication, time management, and teamwork. It’s also crucial that a Machine Learning Engineer is committed to lifelong learning. Given how rapidly the fields of artificial intelligence, deep learning, machine learning, and data science are changing, continuing education is necessary for any professional who wants to stay on the cutting edge.

What Tools Do Machine Learning Engineers Use?

Not only should Machine Learning Engineers possess knowledge of how to code and develop in programming languages such as Python, Java, and C++, many machine learning engineers also find it helpful to master the following tools and resources:

  • TensorFlow
  • Spark and Hadoop
  • R Programming
  • Apache Kafka
  • MATLAB
  • Google Cloud ML Engine
  • Amazon Machine Learning

Machine Learning Engineer Career paths

Few tech professionals begin their careers as Machine Learning Engineers. Most who eventually pursue jobs in machine learning switch over from a role like Software Engineer, Software Programmer, Software Developer, Data Scientist, or Data Engineer.

The two most common paths to becoming a Machine Learning Engineer often wind through data or software development. Once someone has a few years of experience as a Machine Learning Engineer, they could start applying for more senior-level jobs or they could choose to specialize in niche roles relating to data, automation, cloud computing, and machine learning. For instance, demand is currently high for tech professionals who specialize in natural language processing – look for roles like NLP Scientist or NLP Engineer – while Human-Centered Machine Learning Designer is another role that could appeal to someone with advanced machine learning knowledge.