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Easiest ways to get machine learning engineer jobs

July 12, 2023
machine learning engineer jobs

From Amazon’s Alexa to Netflix’s recommendations, there are tech experts in charge of creating the every system we commonly interact with. For businesses, enhancements like understanding user patterns and predicting possible outcomes can be achieved through machine learning engineer jobs. Not sure how to get started in this field? Let’s break down the specifics and how to land a job in today’s challenging industry.

What is machine learning?

As part of artificial intelligence (AI), machine learning (ML) relies on data to analyze patterns and create algorithms. Just like its name implies, the purpose is to replicate what “learning” would entail by retrieving information to perform complex tasks. Through this input, it is possible to predict outcomes of data-based problems as well as automate a series of procedures. Think of it as a way of enabling computers to learn by themselves without the need for human intervention or programming. From banking to retail, machine learning engineer jobs can be employed to:

  • Identify customer patterns and profile specifics
  • Provide investment and financial advice
  • Target ad campaigns based on clients’ preferences
  • Enable customer support chatbots
  • Prevent fraud and security breaches

What’s role does a machine learning engineer play?

Overall, machine learning engineers evaluate data streams to design self-running software, thus enabling the predictive models AI employs. 

Alongside an in-depth knowledge of ML frameworks like TensorFlow and Theano, they handle programming languages such as Python and Java. Moreover, with a solid background in computer science, math, and statistics, these professional’s key roles and responsibilities consist of:

  • Searching and selecting proper data sets employed for learning techniques
  • Conducting statistical analyzes of models to improve functionality
  • Optimizing already existing ML frameworks and increasing scalability
  • Collaborating with data scientists to assess machine learning models
  • Leading a strategic approach towards business requirements

Is this the right field for me?

Working towards innovation while thriving in a fast-paced industry with exceeding potential is exciting. And benefits like remote job options, flexible work schedules, and high-paying salaries make the field sound very appealing. 

However, it must be noted that this is also a demanding, complex, and competitive role. Besides technical knowledge regarding ML frameworks, libraries, tools, and programming languages, here are three more aspects to take into consideration.

1. A strong set of communication skills

Scratch the idea of IT professionals working in complete isolation for days on end. The reality is engineers collaborate with several peers to discuss common project goals and provide constructive feedback. Moreover, team members are sometimes expected to participate in demos to explain technical concepts and provide insight to clients. 

As a machine learning engineer, you’ll also be meeting with stakeholders to share updates on the project’s stages and discuss the best viable options to achieve the intended goals.

2. Eager for knowledge and constant learning

As technology continues to impact more and more aspects of life, there’s a growing need to exceed the latest standards. To meet clients’ requirements in an ever-changing industry, professionals must keep up to date with emerging software, tools, and trends. 

For instance, this means conducting your own research, attending conferences, or sharing valuable content. All in all, the idea focuses on which assets you employ for staying competitive and knowledgeable.

3. A mindset for facing new and unique challenges

Machine learning requires professionals to approach each task and data set through logic-based thinking. Combined with multiple projects and deadlines, the challenges increase. Moreover, once models are designed through extensive research and testing, they may become useless with the rise of new data. 

Navigating this environment can be stressful without proper analytical, organizational, and creative skills.

How to apply for machine learning engineer jobs?

As there’s a high demand for IT professionals, one may think job openings are lining up left and right. However, keep in mind this is a competitive field. 

According to the U.S Bureau of Labor Statistics, as of 2020, there were 10.2 million STEM workers in the country. Today’s industry raises new questions regarding how to stand out from other candidates and land the right job. 

While challenging, there are a few takeaways to consider when looking for a machine learning engineer position.

  1. Build a portfolio that showcases your skills, expertise, and training to gain visibility among recruiters and the tech community.
  2. Do your company research and strategize on the best option for you, like data-driven businesses or machine learning jobs via remote.
  3. Take a hands-on approach to projects as well as research to have a deep understanding of both concepts and workflows.
  4. Get involved through internships and programs, thus creating a professional network while gaining insight into the field.
  5. Think outside the box when searching for a job position. Non-tech companies are also in need of AI and machine learning.

It is important for you to know that some of the most well-known frameworks machine learning engineers work with include TensorFlow, Spark ML, Torch, and Keras. Also, the average machine learning engineer’s salary in the U.S. rates around $145,296 per year, but this may vary depending on specifics like the location, experience, position, and company.Want to get more info on this topic or connect with ML engineers? Be sure to check out our LinkedIn or visit us at

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