What is a machine learning engineer?
A machine learning (ML) engineer is a tech wizard who makes computers smart. They use special skills from computer science and data expertise to teach machines how to learn things by themselves from data. Imagine having a robot that learns to make decisions! These engineers are super important because they help use big amounts of data to make cool and useful things happen, like making apps smarter or helping doctors diagnose diseases faster.
Duties and responsibilities
ML engineers build and look after systems that let machines learn. Their job starts with understanding what problem they need to solve and finding the right data. They then create models—think of these as recipes—that help the computer learn how to solve the problem. They have to keep checking to make sure everything’s working right and update things when needed.
They also work a lot with data scientists to make sure other apps can talk to their smart systems. They always have to make sure their projects can handle lots of data without breaking.
Work environment
ML engineers usually work in offices, but they can also work from home, especially now when lots of people work remotely. They need powerful computers and special software to do their job. They often work on their own solving tough problems but also team up with other techies and business people to come up with the best solutions.
Typical work hours
They typically work from 9 am to 5 pm, but sometimes they might need to work extra to meet deadlines or fix sudden problems. Since a lot of their work can be done on the computer, they often have the option to work from different places, not just the office.
How to become a machine learning engineer
Want to teach computers to learn on their own? Here’s a step-by-step guide on how to become a machine learning engineer:
Step 1: Finish high school
Start by graduating from high school. Pay extra attention to math, physics, and computer classes because they’re super important for this job.
Step 2: Earn a bachelor’s degree
Next, go to college and get a bachelor’s degree in computer science, math, information technology, or something similar. This will teach you about algorithms, how to organize data, and programming — all key for an ML engineer.
Step 3: Dive deeper into machine learning
While in college, take specific classes on machine learning, data mining, and recognizing patterns in data. These are advanced topics that you’ll use a lot in the job. You can find these courses at universities or even online.
Step 4: Consider a master’s degree (it’s optional)
Thinking about studying more? A master’s degree in machine learning gives you deeper knowledge and can make you stand out in job applications. But remember, this step isn’t required.
Step 5: Gain real experience
Try to get involved in projects or internships related to machine learning. This practical experience makes what you’ve learned in class real and helps build a portfolio to impress future employers.
Step 6: Learn key programming languages
You’ll need to know programming languages like Python, R, SQL, and Java. Being good at these languages is crucial for creating and managing smart systems.
Step 7: Get certified
Getting a certification in machine learning or data science can be a plus. These certificates show that you know your stuff and help you catch the eye of recruiters.
Step 8: Apply for jobs and nail the interviews
With your education and experience in hand, start applying for machine learning engineer jobs. Be ready for interviews where you’ll show off your technical skills and how you solve problems.
How much do machine learning engineers make?
ML engineer salaries vary by experience, industry, education, location, and organization size. Their compensation can significantly increase with proven skills in advanced algorithms and programming languages, as well as higher education degrees like a Ph.D.
Highest paying industries
- Information Services and Data Processing: $139,170
- Computer Systems Design: $138,200
- Scientific Research and Development Services: $136,040
- Software Publishers: $135,330
- Management of Companies and Enterprises: $133,530
Highest paying states
- California: $139,620
- Washington: $138,400
- New York: $137,900
- Virginia: $136,960
- Massachusetts: $136,700
The average national salary for a Machine Learning Engineer is:
$132,370
Types of machine learning engineers
Want to know what kinds of machine learning engineers there are? Let’s break it down:
Algorithm specialist
These are the puzzle solvers who create the “brain” of machines. They write complex rules (algorithms) that help machines learn from data and make smart decisions. They need to be really good with numbers and understand data super well.
Data scientist
Data scientists use ML to dig into huge piles of data and find cool insights. They build models that can predict trends and solve problems. Think of them as data detectives who are also good at math and making pretty graphs to show their findings.
Deep learning engineer
These engineers work with advanced AI that tries to mimic how humans think. They use something called neural networks to teach machines to recognize patterns and get better over time. If you’re into coding and solving tough problems, this could be for you.
Natural language processing (NLP) engineer
NLP engineers teach machines to understand and speak human languages. They work on awesome tech like voice recognition (like Siri or Alexa) and translating languages. If you like both words and coding, this might be your thing.
Computer vision engineer
These engineers focus on helping machines “see” and understand pictures and videos. They make it possible for computers to recognize faces, detect objects, and even help doctors read medical images. It’s like giving computers eyes!
Top skills for machine learning engineers
Curious about what skills you need to become a machine learning engineer? Here’s the scoop:
Programing languages
First off, you need to be good at programming. Knowing languages like Python, R, and Java is super important. These help you write the instructions that tell machines how to learn and solve problems.
Math skills
You’ve got to be a math whiz. Skills in linear algebra, statistics, and calculus are essential. They help you understand and build the formulas that make your machine learning projects work.
Understanding machine learning algorithms
It’s crucial to really get how different ML methods work, like how a machine can understand human speech or improve through practice. This knowledge helps you create smarter systems.
Communications skills
You might not think talking is important for coding, but it really is! Being able to explain your techy stuff in simple terms to other people in your team, like marketers or managers, makes your projects go smoother.
Problem-solving
ML engineers are like detectives. You’ll need to figure out tough problems, whether it’s fixing a bug in your code or figuring out the best way to teach a machine to recognize something. Being good at solving problems is a must.
Looking for a new job?
Browse our national database of ML engineer job openings and apply today
Machine learning engineer career path options
If you’re diving into a career as a machine learning engineer, you’re starting on an exciting journey with lots of possibilities. Here’s a look at where you can go from here:
Career progression
Once you get good at your job, you can move up to higher positions like senior or principal machine learning engineer, or even become an architect of big ML projects. In these roles, you’ll manage bigger projects, help guide newer engineers, and make important decisions that shape your company’s future.
If leading a team sounds cool to you, you might become a team lead or head of machine learning. Here, you’ll lead a team and push projects to success. Or, you could become a data science manager, where you’ll mix leadership with heavy-duty data analysis.
Dreaming bigger? Some ML engineers aim to become a chief technology officer (CTO) or even start their own tech company. These paths are about using your tech skills to set the direction for a business and drive it towards success.
Cross-functional opportunities
If you want to try something a bit different, there are cool roles in related areas. Moving into a data scientist role could be a great fit if you like digging into data more than building models. Or, you could become an AI engineer, where you blend machine learning with artificial intelligence to create AI models and apps.
Similar job titles
Position trends and outlook for machine learning engineers
The job of a machine learning engineer is booming! Let’s see what’s happening and what’s coming up in this cool career:
Growing fast
ML engineering is one of the fastest-growing jobs out there. Why? Because more and more companies want technology that can learn and get better on its own. From chatbots that help you shop online to smart systems that make big decisions, machine learning is popping up everywhere. Even car companies and hospitals are jumping on board, using this tech to develop self-driving cars and better ways to treat patients.
Skills in demand
One thing that’s super important in this field is knowing the right programming languages. Python is a big favorite because it’s easy to use and has tons of tools that make building machine learning systems a breeze. Java and C++ are also getting popular, especially for projects that need to make quick decisions. Being good at several of these languages can really make you stand out.
Employment projections
The need for ML experts is expected to keep growing way faster than lots of other jobs. As more businesses start to use machine learning, there will be even more opportunities for smart, skilled engineers. It looks like this trend isn’t slowing down anytime soon, so it’s a great time to get into this field.
Machine learning engineer career tips
Learn the right tools
Start by getting really good at programming languages like Python, C++, and Java. These are the languages you’ll use to create smart systems. Also, get to know tools like TensorFlow and PyTorch. These help you build and run your ML models.
Understand data inside out
Knowing how to handle data is super important. Learn how to clean, select, and change data to make your models work better. This skill is a big part of your job.
Keep up with new stuff
Machine learning changes fast! Make sure you read up on the latest discoveries and studies. This keeps you ahead of the curve and ready to use the newest ideas in your projects.
Talk the talk
Believe it or not, being able to explain your techy stuff to others is crucial. You’ll need to share your ideas with people who don’t know coding or data science, so good speaking and writing skills are a must.
Network, network, network
Meeting other people in your field can open up new opportunities and help you learn more. To connect with other smart folks, join groups like:
- Association for the Advancement of Artificial Intelligence
- Professional Association for SQL Server
- Data Science Association
Never stop learning
Because tech keeps evolving, you should too. Keep learning new things through courses and certifications. Check out programs like:
- Professional Certificate in Deep Learning from IBM
- Machine Learning Certification by Stanford University (Coursera)
- Postgraduate Program in AI and Machine Learning (Purdue University)
Where the ML engineer jobs are
Top employers
- Microsoft
- Amazon
- Apple
Top states
- California
- Washington
- New York
- Texas
- Massachusetts
Top job sites
- zengig
- Indeed
- Monster
- AI Job Pro
FAQs
What are some of the key skills required to be successful as an ML engineer?
Successful practitioners in this field display a strong foundation in computer science, statistics, and mathematics. Beyond specific technical skills, effective problem-solving capabilities, an analytical mindset, and a curiosity about patterns and trends are also crucial.
What is a typical workday of an ML engineer??
A typical day could involve data exploration and cleaning, model building, testing algorithms, interpreting and communicating results, or collaborating with other team members on larger projects. The role is quite varied and requires adaptability and broad skills.
Which industries employ machine learning engineers?
This profession is prevalent in several areas due to its versatility. ML jobs are common in technology but fundamental to healthcare, finance, transportation, and manufacturing, among other industries.
What are the typical education requirements for an ML engineer?
This role typically requires a bachelor’s degree in computer science, mathematics, or a related field. However, due to its highly specialized nature, many employers prefer candidates with advanced degrees focusing on machine learning or artificial intelligence. Proficiency with specific programming languages such as Python or Java is often required as well.
What part does teamwork play in ML engineering?
Although this profession involves a lot of independent research and problem-solving, it’s essential to communicate effectively with other team members. You’ll often need to collaborate with other engineers and stakeholders to complete projects or clarify business objectives.
Are there ML certifications available that will improve marketability?
Certifications can highlight your expertise and dedication to the field. Consider pursuing credentials like the TensorFlow Developer Certificate or the Microsoft Certified: Azure AI Engineer Associate to enhance your profile and stand out in this competitive profession.
What is the career progression in machine learning?
Entry-level roles typically involve working under the supervision of a senior engineer or lead. As you gain more experience and demonstrate advanced skills, you may move into roles with more responsibility, eventually leading a team or overseeing a department. This profession also offers opportunities to move into fields like data science or artificial intelligence research.
What can I do to stand out in my job applications as a machine learning engineer?
Beyond relevant education and certifications, highlighting real-world experience can make your resume stand out, including internships, relevant research projects, or even personal projects utilizing machine learning. Demonstrating your problem-solving abilities through specific examples can also make a strong impression.
How important is continuous learning in machine learning?
Continuous learning is critical due to the rapidly evolving nature of technology and machine learning applications. A passion for learning and the capability to adapt to new paradigms and tools is vital for long-term success.