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No matter which industry you work in, it is undeniable that the presence and importance of machine learning (ML) has increased exponentially. By automating tasks, increasing efficiency, and enabling data-driven decision-making, ML technologies have revolutionized how businesses operate worldwide.

As the field continues to grow and evolve, you may be wondering: are there any job opportunities available within machine learning? Is it even a viable and lucrative path to follow? If you’re interested in joining this emerging field or want to learn more about what a career in machine learning looks like, then you’ve come to the right place. This article will serve as a guide for aspiring machine learning engineers and will discuss the different educational pathways, essential skills, and practical experience needed to succeed in the field.

Understanding the role

Before we dive into how to begin your journey in machine learning, let’s first establish what ML is and what exactly ML engineers do. Machine learning is a subfield of artificial intelligence (AI) that enables computers to interpret and analyze data through the development of algorithms. The main objective is for computers to be able to identify patterns and accurately make forecasts, without explicit programming.

With this definition in mind, a machine learning engineer is the person behind the scenes who actually designs and builds the machine learning algorithms and models. Machine learning engineers are responsible for researching and testing these systems, as well as developing prototypes before ultimately deploying the finalized algorithms and training computers with them.

The significance and value of the machine learning engineer in today’s tech-driven world is unparalleled. Here are some more ways that ML engineers have made a tangible impact in different industries:

  • Transforming retail by learning consumer behaviour and driving personalized recommendations
  • Identifying patterns in healthcare data to create diagnoses and predict patient treatments
  • Detecting fraud in the finance sector to prevent losses and improve profitability

Educational pathways to become a machine learning engineer

If the machine learning engineer job description above piqued your interest, congratulations! You’re definitely heading in the right direction now. To begin your career path in machine learning, the very first step is to get a foundational educational background that will set you up for success.

University degrees

There are many different avenues you can take to get this knowledge, with the most traditional way being to get a degree in a related field like computer engineering, statistics, data science, or mathematics.

There are a few pros and cons to going the academic route, though: for one, it’s typically a bit more expensive in the long run, as each year of school can cost thousands of dollars. It is also probably the most time-consuming and rigid option, as bachelor’s degrees usually have a 4-year minimum where students have strict schedules. On the other hand, getting a degree from a recognized institution can be an incredibly prestigious achievement that will earn you significant credibility when applying to jobs.

Bootcamps & online courses

If you’re interested in exploring other educational pathways, another avenue that is increasing in popularity is the option to enroll in bootcamps or online courses. For those looking for a quicker way to launch their career, programs like the Data Science Bootcamp at Lighthouse Labs can be a great option to consider. The program can be completed in as little as 12 weeks in the full-time intensive Bootcamp, or 30 weeks in the flexible part-time Bootcamp. Our programs also offer flexibility in the sense that they are fully online, meaning that students who are balancing multiple priorities can breathe easy knowing they can complete lectures remotely from wherever.

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When it comes to comparing bootcamps vs. degrees, there is no objective answer as to which one is better. It is truly a matter of preference based on your unique learning needs and the specific requirements of the role you are interested in. For example, if you like to learn in person in a physical classroom, you may want to consider enrolling in a university where you live on campus. However, if you prefer being remote, a virtual bootcamp may be a better fit for you.


On this note, though, another online pathway to consider is the option to self-study: this can look like completing free online courses, like the Python Crash Course at Lighthouse Labs, on your own time. This avenue may be right for you if you are a disciplined student looking for a cost-effective option where you can go at your own pace. Take a look at the table below to evaluate and compare the pros and cons of these three different educational pathways.

  University degrees   Bootcamps & online courses   Self-guided study
  • Prestigious
  • Fast-tracked
  • Flexible
  • Cost-effective
  • Time-consuming
  • Rigid
  • Intense
  • Unstructured

Essential skills and tools for a machine learning engineer

While foundational knowledge is the key to getting started in ML, there are plenty of additional technical and soft skills necessary for success in the field. Beginning with the technical side, there are many tools and programming languages that machine learning engineers will need to become familiar with. Most online bootcamps (like the ones at Lighthouse Labs) will usually cover all of the basics you need to know, but if you really want to become a master, you may need to put in some extra hours practicing on your own time. Some important tools and programming languages to know include:

  • Python: Python is a high-level, multi-purpose coding language that has become incredibly popular. Since the program is so general and user-friendly, it can be used for anything from recommendation algorithms to web development and data analysis. The world is truly your oyster with Python! If you want to learn how to use the program, Lighthouse Labs offers a free Python Crash Course for beginners that can be completed in 30 hours.
  • SQL: SQL, which stands for Structured Query Language, is a program that is widely used for accessing and modifying relational databases. A relational database is basically a table of information that shows rows and columns full of data attributes. Since ML engineers rely on data to build algorithms and models, SQL is an invaluable tool to know. Learn SQL Essentials at no cost to you with Lighthouse Labs.

Although these technical skills are definitely fundamental for those looking to break into machine learning, you won’t be successful in the industry with your coding wizardry alone. There are also a number of vital soft skills and personality traits that companies look for when hiring ML engineers. Take a look at the list below:

  • Problem-solving: Since machine learning engineers are often working with complex data, there are sure to be challenges and obstacles that come up, including broken or missing data sets. When faced with issues like these, engineers need to be resilient and solution-oriented.

  • Teamwork: Machine learning engineers will have to work alongside other team members, both fellow engineers and non-technical team members. With this in mind, prospective ML engineers should be comfortable working both independently and in larger teams, and should have the cultural competence and collaborative skills to work with people from all walks of life.

  • Time management: As in any line of work, it is expected that machine learning engineers will need to be comfortable juggling multiple projects alongside tight deadlines. Applicants should be experts in prioritizing tasks and managing their time.

  • Adaptability: Since ML and AI are fast-paced fields, things will inevitably change quickly when you are on the job in the real world. Successful machine learning engineers should be easily adaptable and open to change. They should also have a passion for life-long learning.

Gaining practical experience

Once you’ve established your theoretical knowledge, the next step to becoming a machine learning engineer is to gain some hands-on and real-world experience through things such as projects or internships. Although education is definitely important for breaking into the world of ML, it is also crucial to have some practical experience around your belt. This will give employers a first-hand look at how you can apply your knowledge in real-world situations.

How exactly can you gain this experience, you may ask? Well, if you are self-motivated and passionate, consider taking the initiative to create your own projects in your free time. This can include designing a sales forecast, predicting stock prices or creating a sentiment analysis. Once you’ve finished, you can create a project portfolio on websites like Github to store all of your work. Once you begin applying to jobs, put this link on your resume so employers can easily access your previous projects and see your valuable skills in action.

If you’re someone who needs a bit more structure, try looking for an ML internship or apprenticeship where you can shadow a real machine learning engineer. If you have a little bit of experience and background knowledge, you can find open internship positions on job boards like LinkedIn or Indeed. Many internships offer placements for students ranging from 4 to 8 months, and these opportunities are typically paid. For those who are more on the beginner end, try searching for ML engineers in your area on LinkedIn, and send them a message proposing a voluntary apprenticeship. If you have the privilege of having another source of income and you can dedicate some of your free time to shadowing a pro, this kind of hands-on experience can prove to be incredibly valuable.

Advancing your career

As touched on in the essential skills section above, upskilling and continuing education are vital parts of the machine learning career path: I mean, the word ‘learning’ is in there for a reason! There are several ML engineer certifications you can take to advance your career, including ones from IBM, Google, and Amazon Web Services (AWS). There is a fee for these certifications (ranging from $49 to $300), but taking the time to complete them can help advance your career in machine learning and can potentially lead to more senior job opportunities. If you’re interested in more traditional academia, you can also consider pursuing a master’s degree or PhD that is specialized in machine learning to gain more credibility and move up in your career.

Another aspect that is vital in developing your career is networking. If you’re thinking of transitioning to machine learning, the best place to start may be within your own circle of friends and family, or any professional organizations you are part of in your local community. Although many job openings and roles are typically posted online on job boards like LinkedIn and Indeed, sometimes new roles are not posted to the public. By building strong connections and relationships in the ML field, you can open the door to potential job opportunities. As well, networking can help you find mentors who can support your development, offer industry knowledge and resources, and who can even introduce you to their own network of contacts.

Common challenges and how to overcome them

A career in machine learning can be very rewarding and fruitful, but as with any job, it can also come with its unique challenges. Not to worry, though: as long as you are prepared for the obstacles ahead, you can learn to overcome them. Here are some common challenges you might face in your career as a machine learning engineer:

  • Job market competition: When submitting your machine learning applications, you may see that some job postings have hundreds of applicants. While this competitive market can certainly be discouraging (especially if you are early on in your career), the best thing to do is to keep levelling up and enhancing your application. While on the job hunt, make sure to keep adding to your portfolio and find a way to make it stand out from the rest. You can also try attending industry networking events and leverage your unique network to discover opportunities that may not be available to the public.
  • Keeping up with technological advancements: As we’ve discussed, the tools and platforms used in ML can change quite rapidly and frequently. Although it can be frustrating to constantly have to stay up-to-date, the best way to tackle this upskilling challenge head-first is by getting ahead of it. In your free time, make sure to follow and read the latest tech news so when updates come or trends evolve, you won’t be surprised.

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  • Communicating with stakeholders: Once you are in a machine learning engineer role, you may find that, depending on the company or organization, you may have to present in front of senior leadership or executives. Although skilled engineers understand the value behind their work, it can be difficult to explain the value of ML to a non-technical team. To solve this issue, consider presenting your work by demonstrating how ML is helping the business ladder up to greater goals. Make sure to also limit your use of jargon and focus on discussing key points in layman’s terms for easy understanding.

Introduction of the updated Lighthouse Labs Data Science Bootcamp

If you’re interested in starting your journey towards becoming a machine learning engineer, another aspect to keep in mind when considering educational pathways is to ensure you are receiving the most up-to-date and relevant information. At Lighthouse Labs, we know that the machine learning space continues to rapidly evolve and develop, which is why we recently updated our Data Science Bootcamp to include a 40-50 hour course on LLM/NLP usage in the context of data science. The newly updated Bootcamp takes a modern approach by focusing on solving business problems that are likely to come up in the real world. On top of LLM/NLP knowledge, you will also learn key topics such as data visualization, data wrangling, and math foundations. Download our in-depth curriculum to find out more about what you’ll learn during the Bootcamp.

Beyond this foundational knowledge, enrolling in a Lighthouse Labs Bootcamp also gives you access to on-demand mentorship, weekly check-ins with our Student Success Coordinators, and lifetime career support with our Career Services team. You’ll also become part of an engaged community of Lighthouse Labs grads who regularly keep in touch via Discord and often join alumni events including Demo Days and guest speaker opportunities.

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What is the average ML engineer's salary?

A machine learning engineer can expect to earn anywhere from $69,000-$150,000+ depending on experience level.

What can I expect in an ML engineer interview process?

Although the interview experience may vary at different companies, you can likely expect to begin with a short phone screening with a recruiter. After you apply to a machine learning role, this first step typically helps human resources get to know you and will filter out any unqualified candidates. This screening usually takes no more than 15-20 minutes.

If you are successful in the phone screening, you may be asked to complete an at-home/take-home assignment or test to demonstrate your technical skills and knowledge. Tasks can include building a machine learning model based on provided data or analyzing a data set. You will usually have a specific time limit in which to complete, typically in a range of 3-8 hours.

Lastly, if you are successful in the assignment, you will likely move on to a final round interview with the hiring manager. Depending on the company, this interview may be virtual or in-person. This interview typically focuses more on soft skills, as the hiring manager will be looking to see if you are a good fit for the company beyond your technical qualifications. Be prepared to answer behavioural questions that showcase your personality and critical thinking skills by drafting some examples of previous experiences.

Is there a high demand for machine learning engineers?

Absolutely! There is definitely a need for skilled machine learning engineers, with some industries having a more pronounced demand than others. Some machine learning job trends right now are an increased demand for engineers in the healthcare, finance, and retail sectors, as well as a big emphasis on remote positions. As more businesses turn their focus to AI and automation, the demand for machine learning roles will only continue to increase.

Where is the best place to find ML engineer job openings?

There are many ways to find machine learning engineering opportunities. The first is by going to general job boards like LinkedIn, Indeed, and Glassdoor. There are also specialized job boards that only post roles in tech, such as Wellfound. It is also a good idea to keep an eye on the websites of specific companies you may be interested in, as they also often post jobs directly to their career site. If you are looking for more freelance work, another great place to find available ML opportunities is on platforms like Upwork.

Another avenue to find a ML engineer job can be to leverage your own network of professional connections. As noted earlier, building relationships with machine learning professionals may lead to opportunities that are not yet available to the public, or that are not yet even created! In this context, it is important to build genuine and meaningful connections: even if you are not looking for a role right now, make sure to regularly stay in touch with your network so that when you _do _begin the job hunt, you’ll have warm leads that are more inclined to help.

Does machine learning require coding?

Yes, coding is a key skill to have if you want to become a machine learning engineer. Coding expertise in programs like Python is the foundation for many of the tasks that ML engineers have to do, including algorithm development and optimization. There are also many other job titles that require coding knowledge, including software engineers, data scientists, and AI engineers.

Are you ready to transform your career? Sign up for our cutting-edge Data Science Bootcamp and begin your path to becoming a machine learning engineer today. Download our Data Science Curriculum here and take the first step towards mastering the skills that will define your future in tech.