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How to become a Machine Learning Engineer in 2024

By Katie LoFaso

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Contents

  1. How can I become a Machine Learning Engineer?
  2. What is machine learning? An overview of the field
  3. What do Machine Learning Engineers do?
  4. Machine learning: Salary and job outlook
  5. Machine Learning Engineer FAQs
  6. Unlock the future of your technical career with Multiverse

We’re all consumers of apps and data products. So, machine learning (ML) has become an integral – though often unrecognized – part of daily life.

When you use Google Maps(opens new window) to drive to a doctor’s appointment, ML algorithms analyze historical traffic patterns and recommend the fastest route. If you open Spotify(opens new window) for the drive, the platform uses ML to curate a personalized playlist of your favorite songs based on your listening habits and in-app behavior.  And once you arrive at the appointment, ML can even help your doctor analyze your medical images(opens new window) and provide an accurate diagnosis.

As the number of ML applications has increased, so has the need for Machine Learning Engineers and other artificial intelligence (AI) specialists. Between 2023 and 2027, the World Economic Forum’s Future of Jobs Report 2023(opens new window) predicts the demand for these professionals will increase 40%.

The rapid proliferation of machine learning jobs has led to a significant skills gap. Many professionals are only just dipping their toes in this exciting field, and educational opportunities are still emerging. As a result, 75% of employers(opens new window) focusing on hiring people with AI skills have struggled to find qualified candidates.

You can take advantage of this gap by pursuing a career as a Machine Learning Engineer. But, there are a few factors to consider before you commit to this field. Below, we’ll explore a Machine Learning Engineer’s responsibilities, job outlook, salary, skills, and more.

How can I become a Machine Learning Engineer?

There’s no one-size-fits-all career path in machine learning engineering. Many professionals learn relevant ML skills while pursuing a computer science or data science degree, and some of them will even have advanced degrees in a highly technical or specialized field like ML. That said, a college education isn't strictly necessary provided you have the right combination of skills and experience.

Here are five ways you can prepare for a career as a Machine Learning Engineer – without a traditional college degree.

Learn relevant technical skills

Employers might prioritize technical expertise over formal education when hiring for the most in-demand roles. So while many AI and ML engineers will have college degrees, mastering the right skills can help put you on a path to secure employment in this fast-growing field — even without college.

Skill requirements vary by employer and role but often include:

  • Machine learning algorithms - You’ll need a firm understanding of supervised and unsupervised learning algorithms. For example, cluster algorithms use unsupervised learning to detect similarities between data points and organize them into groups. Meanwhile, regression algorithms use supervised learning to predict future outcomes, such as interest rates and the demand for concert tickets.
  • Python - ML Engineers typically use Python to process data and write code for machine learning applications. This popular programming language has many machine learning frameworks and libraries to accelerate coding. For example, Engineers use Theano to train deep learning models and PyTorch to develop neural networks.
  • Other programming languages - Some machine learning roles require knowledge of additional programming languages. ML Engineers may use C++ to create data structures and Java to write large-scale algorithms.
  • Data modeling - ML Engineers frequently use algorithms and statistical methods to build data models. These representations identify patterns in data and may predict future trends. For instance, a hospital could create a predictive model that forecasts future outbreaks of infectious diseases based on patient records and vaccination rates.
  • Natural language processing (NLP) - Understand how to teach machines to interpret and respond to complex human language. The Natural Language Toolkit can help you build natural language processing programs with Python.

Machine learning professionals also need strong soft skills. Problem-solving skills allow you to approach challenges from multiple perspectives and brainstorm creative solutions. Collaboration skills enable you to work on large-scale projects with Data Engineers, Data Scientists, Software Developers, and other experts.

Online courses can help you develop these essential skills. An apprenticeship can also help you prepare for a Machine Learning Engineer role. Multiverse’s Software Engineering Apprenticeship teaches full-stack development, algorithmic problem solving, and other technical skills. In addition, apprentices learn programming languages related to their career goals, such as Python and C++.

Multiverse apprentices earn a competitive paycheck while they learn and work for a top employer. Apprentices work with their managers to complete deep dive electives that prepare them for future careers. You can also ground yourself in foundational AI skills through Multiverse’s asynchronous AI Jumpstart Module. These immersive experiences let you quickly gain the necessary skills and hands-on experience for a machine learning career.

Apply skills through projects

As you develop foundational knowledge, take your skills to the next level by creating machine learning projects. These tasks will strengthen your understanding of machine learning techniques and concepts, which can seem abstract or unclear in theory. They also let you practice applying your skills in real-world scenarios that you may encounter in the workplace.

Here are a few beginner-friendly ML projects:

  • Use Kaggle’s Titanic dataset(opens new window) to build a predictive model about the most likely passengers to survive.
  • Use local housing data to create a regression model that predicts rental prices in your city.
  • Design a computer vision application that identifies digits in the MNIST data set(opens new window).
  • Develop an NLP model that analyzes your favorite books and generates similar stories.

Many websites publish appropriate data sets for ML projects. Popular resources include Data.gov, Data USA, Kaggle, and the UC Irvine Machine Learning Repository.

Showcase your best projects in a digital portfolio. Include a brief description of each project, an explanation of the machine learning processes you used, and the final product. These samples will help you demonstrate your skills to potential employers.

As your skills develop, you can also use your portfolio to market your services as a freelance ML Engineer. Look for freelancing roles on Freelancer, LinkedIn, Upwork, and other websites. These positions can help you gain relevant experience and grow your professional network.

Earn an entry-level role in a related field

It can take time to qualify for a machine learning engineering role, especially if you don’t have prior tech experience. But you can get your foot in the door by landing an entry-level role in a closely related field. These jobs allow you to make valuable professional connections and gain relevant technical skills.

Many Machine Learning Engineers begin their careers as Software Engineers. This career requires knowledge of computer architecture, programming languages, and software systems.

A data science role can also lay the foundation for machine learning careers. The typical responsibilities of a Data Scientist include data collection, data cleaning, and model development. These duties will help you prepare to create machine learning systems.

Continue building your resume

Once you’ve broken into the tech industry, add relevant qualifications and skills to your resume.

Certifications are one of the fastest ways to demonstrate your machine learning expertise. Many professional organizations offer relevant credentials for ML beginners. For example, the IBM Machine Learning Professional Certificate(opens new window) and the Microsoft Certified: Azure Data Scientist Associate(opens new window) verify that recipients have job-ready ML skills.

Participating in machine learning challenges can also strengthen your resume. Kaggle and ML Contests frequently host competitions where participants vie for cash prizes. Additionally, websites like MachineHack organize interactive hackathons. These experiences can help you expand your ML and data science skills.

Apply for ML-specific jobs

Once you’ve gained a solid foundation in machine learning, you’re ready to tackle the job market.

Search for Machine Learning Engineer jobs on Indeed, LinkedIn, and other job boards. You should also look for positions with similar titles, such as Deep Learning Engineer and Computer Vision Engineer.

Prepare for job interviews by researching common questions and rehearsing your responses. For example, you may need to explain how bias and other factors affect model performance.

What is machine learning? An overview of the field

Machine learning is a branch of artificial intelligence that teaches computer systems to learn from data without explicit human intervention. Machine learning algorithms detect relationships between datasets and then extrapolate their findings to make predictions and perform tasks.

Like humans, ML algorithms continuously learn and improve as they gain experience. For example, Spotify delivers increasingly accurate song recommendations as users listen to and interact with more music.

What do Machine Learning Engineers do?

The job responsibilities of a Machine Learning Engineer might include:

  • Collaboration - A Machine Learning Engineer often works closely with a larger data science team. Say, for instance, a Data Engineer builds a data pipeline to gather and process data from the stock market. A ML Engineer could collaborate with a Data Scientist to use this information to develop predictive models that forecast stock prices.
  • Create data science prototypes - Professionals develop prototypes of algorithms or machine learning models to explore different approaches and make improvements. For example, an ML Engineer can create prototypes of NLP models that use different techniques to analyze the sentiment of customer reviews.
  • Train machine learning algorithms - ML experts use supervised, unsupervised, or reinforcement learning to teach algorithms to perform tasks. For example, they could use supervised learning to teach a model to analyze medical imaging(opens new window) and determine if it shows malignant or nonmalignant tissue. Healthcare providers can use the model to improve cancer diagnosis.
  • Feature engineering - ML professionals use statistical methods to select the most relevant and useful variables from raw data. For instance, they could extract cell size, shape, and texture from medical images to teach a model to recognize the characteristics of cancer.
  • Continuous learning - The artificial intelligence industry evolves quickly as new approaches, technologies, and tools emerge. A successful Machine Learning Engineer actively follows these developments. For instance, they might attend an Association for the Advancement of Artificial Intelligence conference(opens new window) to network and learn about industry trends.

Machine learning: Salary and job outlook

According to the Hiring Lab(opens new window), artificial intelligence-related jobs made up 2% of all job postings in February 2024. The number of jobs will likely continue to rise as more companies invest in AI technologies. Statista(opens new window) predicts that the global machine learning market will grow from $79 billion in 2024 to $503 billion in 2020 – a staggering increase of 535%.

Machine Learning Engineers often receive lucrative compensation packages. Here’s the average salary for this career:

  • Starts at - $99,000 with less than one year of experience
  • Median pay for all experience levels - $164,000
  • Top earners - up to $210,000

Source: Glassdoor

Successful Machine Learning Engineers may get promoted to more advanced – and often higher-paying – roles, such as:

  • Lead Machine Learning Engineer - $154,000 to $333,000
  • Senior Machine Learning Engineer -  $160,000 to $349,000

Source: Glassdoor

Machine Learning Engineer FAQs

What coding languages or technologies do Machine Learning Engineers use most?

Python is the most common programming language for machine learning engineering. This language has thousands of libraries for data analysis, data mining, deep learning, and other machine learning tasks. For example, Machine Learning Engineers can use Scikit-learn to build supervised and unsupervised models.

Additionally, many machine learning professionals use R for exploratory data analysis and model fitting. Julia is another useful language for large-scale data processing and fast prototyping.

Is ML a good career choice for me?

Machine learning engineering could be a good fit if you enjoy learning about cutting-edge technology and working with data. This career path requires strong analytical, coding, mathematical, and problem-solving skills. You’ll also need a keen eye for detail and the patience to continuously test and refine models for the best performance.

Unlock the future of your technical career with Multiverse

Machine learning has quickly become ubiquitous in daily life, but experts have only just begun to uncover its potential. Future advancements in machine learning may profoundly alter healthcare, logistics, science, transportation, and other industries.

Looking to break into a technical career? Multiverse’s 15-month software engineering apprenticeship can help. Unlike college, you’ll actually get paid to learn coding, full-stack development, and other computer science principles. You’ll also gain hands-on experience working for a leading company. Additionally, all apprentices get access to our AI Jumpstart modules, which covers the core fundamentals of AI.

Complete our simple application(opens new window) today to take the next step on your machine learning journey.

Katie LoFaso

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