What does a machine learning engineer do?

What is a Machine Learning Engineer?

Machine learning engineers are IT professionals, part software engineer and part data scientist. They use their coding and programming skills to collect, process, and analyze data – with the goal of creating tools that will make that data easier to collect and classify. They design machine learning systems, creating and applying algorithms to generate accurate predictions, and utilizing machine learning to help organize data and resolve data set problems.

Machine learning engineers live on the cutting edge of technology, and the ML systems they build are used in every other big data job.

Machine learning (ML) is a part of the computer science field specifically concerned with artificial intelligence (AI). It is the implementation of AI and algorithms to interpret data that replicates how humans learn. Once an ML system is built, it can essentially self-run, adjusting to information and instructions it is given, improving its learning accuracy, and providing data based on that learning to the user. In other words, each time the software performs an operation, it ‘learns’ from those results to carry out future operations more accurately.

Examples of machine learning are facial recognition on smartphones, and the use of algorithms by companies like Facebook and Amazon to target advertisements and suggest items to buy based on users’ / customers’ viewing history and purchases. Customer-facing businesses use ML to better understand consumers’ patterns and preferences and design direct marketing or ad campaigns. Virtual assistants, translation apps, chatbots, and self-driving cars are also forms of machine learning. As all of these examples illustrate, ML systems are powerful tools. But they have to be put in place, they have to be built. That’s where the machine learning engineer comes in.

What does a Machine Learning Engineer do?

Machine learning engineers act as critical members of the data science team. When a bot (short for robots) is used by a particular business for chat purposes or data collection, for example, that bot is created by machine learning engineers. Any algorithms used to sort through relevant data are the work of machine learning engineers. ML engineers also help to scale predictive models to best suit the amount of data relevant to the business. Their day-to-day duties include:

• Consulting with managers to determine and refine machine learning objectives
• Designing machine learning systems and self-running artificial intelligence software to automate predictive models
• Transforming data science prototypes and applying appropriate ML algorithms and tools
• Ensuring that algorithms generate accurate user recommendations
• Turning unstructured data into useful information by auto-tagging images and text-to-speech conversions
• Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks
• Developing ML algorithms to analyze huge volumes of historical data to make predictions
• Running tests, performing statistical analysis, and interpreting test results
• Documenting machine learning processes
• Keeping abreast of developments in machine learning

What is the workplace of a Machine Learning Engineer like?

Because virtually all industries stand to benefit from investing resources into mining insights from data, machine learning engineers can choose to work in any sectors that interest them.

Depending on the size of the organization they join, ML engineers often work as part of a larger data science team, which might include data scientists, data analysts, data engineers, database architects, and database administrators. In addition to being part of a collaborative data team, their responsibilities may involve liaising with an organization’s senior executives as well as marketing, sales, IT, software development, or web development professionals.

The machine learning environment is a diverse and evolving one, which offers exciting prospects and opportunities to make a difference in areas such as healthcare, cyber security, transportation, and marketing, to name but a few.