How to become a machine learning engineer
What’s involved in being a machine learning engineer and how to become one
As more companies bring artificial intelligence (AI) technologies into the fold, machine learning engineers will continue to be among the most highly sought IT professionals. Currently, the job opportunities are plentiful, and as time continues to pass by, organisations will need more trained machine learning engineers.
Statistics, data science, computer science, mathematics, problem-solving, and deep learning skills are all critical for machine learning engineers. And to be a successful machine learning engineer, one must learn an array of programming languages and execute precision when dealing with complicated data sets and algorithms.
There are plenty of online resources to learn from when launching a machine learning engineer career, but the sheer amount of information available can make absorption and retention difficult. It's also tough to choose the right career path for you, as the number of machine learning opportunities continues to grow and spread to many different industries.
To help you on your path to becoming a machine learning engineer, we’ll answer some key questions about getting into the role, including:
- What does a machine learning engineer do?
- What career opportunities are available to machine learning engineers?
- Do I need a degree to become a machine learning engineer?
- What skills do I need to become a machine learning engineer?
- How do I become a machine learning engineer?
Machine learning engineer skills and job requirements
What skills do you need to become a machine learning engineer?
In addition to coding capabilities and hands on experience, machine learning engineers will be expected to possess a number of key skills and proficiencies. There are a number of areas which commonly tend to play a role in machine learning careers, and it's worth making sure you're familiar with as many of them as possible.
- Computer science fundamentals and programming: Build data structures (e.g., stacks, queues, multi-dimensional arrays), apply algorithms (e.g., searching, sorting, optimization), understand computability and complexity (e.g., P vs. NP, NP-complete problems, approximate algorithms), and develop computer architecture (e.g., memory, cache, bandwidth)
- Probability and statistics: Employ techniques used in probability (e.g., Bayes Nets, Markov Decision Processes, Hidden Markov Models), calculate statistical measures and distributions (e.g., uniform, normal, binomial), and apply analytical methods (e.g., ANOVA, hypothesis testing) for building and validating models from observed data
- Data modelling and evaluation: Estimate the underlying structure of a given dataset, find useful patterns (e.g., correlations, clusters), predict properties of unseen instances (e.g., classification, regression), choose appropriate accuracy/error measures (e.g., log-loss for classification, sum-of-squared-errors for regression), and evaluate strategies (e.g., training-testing split, sequential vs. randomized cross-validation)
- Machine learning algorithms and libraries: Find suitable models to apply libraries, packages, and APIs (e.g., Spark MLlib, TensorFlow), create learning procedures to fit the data (e.g., linear regression, gradient descent, genetic algorithms), and develop an awareness of advantages and disadvantages of different approaches (e.g., bias and variance, missing data, data leakage)
- Software engineering and system design: Understand how elements work together, communicate with systems (e.g., library calls, database queries), and build interfaces
Machine learning applications
There are many potential applications that machine learning can be used for across a wide range of industries. Some of the most common examples include:
- Image and speech recognition (e.g., auto-tagging images, text-to-speech conversions)
- Providing customer insights (e.g., noting a customer purchased product 1 and recommending product 2)
- Risk management and fraud prevention (e.g., financial predictions, risk of loan defaults)
Quick facts about working as a machine learning engineer
- The average pay for machine learning engineers in 2020 was $147,134 per year
- Job postings for machine learning engineers have grown by 344% between 2015 to 2018
- Machine learning engineers typically require a master’s degree or PhD in computer science, software engineering, or a related field for the best career prospects
- Most job advertisements in fields involving AI or machine learning are for machine learning engineers
Machine learning engineer jobs
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Given the fact that AI is a field within technology that’s in its relative infancy, both job security and job opportunities are in abundance and expected to grow over the coming years. It’s nigh on impossible to pinpoint an industry that isn’t exploring new innovations in AI, allowing companies to develop new products quicker and in a more streamlined fashion, while embracing the latest and greatest technology.
That said, with the varying skill sets that are demanded by machine learning engineer roles, there are, naturally, plenty of opportunities to specialise in one specific field. There is an array of job roles a machine learning engineer can pursue and it may be worth tailoring your education pathway if one seems more attractive than the others.
Types of machine learning engineer jobs:
- Machine learning engineer: Use of machine learning algorithms and tools to design and develop systems and applications
- Data scientist: Use big data, AI, machine learning, and analytical tools to collect, process, analyse, and interpret large amounts of data
- NLP scientist: Design and develop machines and applications to learn human speech patterns and translate spoken words into other languages
- Software developer/engineer: Design, develop, and install machine language software solutions, create computer functions, prepare product documentation for visualisation, test code, create technical specifications, and maintain systems
- Human-centred machine learning designer: Create intelligent systems to learn individuals’ preferences and behavior patterns through information processing and pattern recognition
Machine learning engineer duties
A data scientist and a machine learning engineer share similar duties: they must carry out complex modelling on dynamic data sets, perform data management, and work with vast amounts of data. Furthermore, they are also expected to design self-running software to automate predictive models, which utilise their previous findings to improve their accuracy in performing operations in the future.
The core duties of a machine learning engineer, though, revolve around building algorithms to improve predictive accuracy and analyse data without human intervention. Machine learning is linked to AI and deep learning, where artificial neural networks use deep data sets to solve complex problems and "think".
Aspiring machine learning engineers could be working on applications for image and speech recognition such as technology used for auto-tagging images and text-to-speech conversion software. Engineers could be working on customer insight products used by businesses looking to understand what a customer is likely to want to buy next. They also may be working on technology used by the likes of financial institutions to prevent fraud or assess the risk of an applicant defaulting on a loan, for example.
Machine learning engineer education and learning path
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Most employers of machine learning engineers will be looking for individuals with a strong educational background, with a master’s degree or PhD in a relevant field being the typical prerequisite qualifications for a decent job.
There aren’t too many dedicated courses for machine learning in the UK, as of now, so degrees in subjects such as mathematics, computer science, electrical engineering, physical sciences, and statistics are all considered. In the US, there is a swathe of top universities, such as Carnegie Mellon, that offer dedicated machine learning curriculums.
Machine learning is unlike software development in that a degree is almost always a requirement to secure a job, although if you can show you have the necessary skills to enter the field, such as demonstrable skills in statistical analysis, exemptions may be made. In these cases, you would typically be expected to have undergone a machine learning-specific course to supplement your understanding of the field. Such courses could include those on an online learning platform or a skills bootcamp like the one offered by Teeside University in the UK.
Going through a master’s degree program will provide programming skills, an understanding of machine learning frameworks such as TensorFlow and Keras, and advanced mathematics skills like linear algebra and Bayesian statistics.
Professional certifications from industry giants like Amazon, or another accredited association, will also help you to stand out in the field.
How do you become a machine learning engineer?
It’s best to develop a strategy before applying for a position as a machine learning engineer. Determine the industry you want to work in and what type of machine learning engineer you’d like to be.
Once you have a relevant undergraduate degree, you might want to get a position with a career path leading toward becoming a machine learning engineer. This could include working as a software engineer, programmer or developer, data scientist, or computer engineer.
While you’re working in one of these careers, you can study for a master’s degree or PhD in computer science or software engineering.
Make sure to stay on top of current algorithms, programming languages, and machine learning libraries. Take continuing education courses and update your professional certifications.
Build your network and learn more about the role by connecting with other machine learning engineers on LinkedIn, which will keep you in the know on job openings and industry expectations. Ask your contacts for advice on building your career as a machine learning engineer.
Tips for applying to machine learning engineering jobs
- Update your knowledge, skills, and certifications on your resume before applying for a job. Highlight the skills advertised in job postings and list your previous accomplishments.
- Write a cover letter to explain how your experience makes you ideal for the role. Describe why you want to work with the organisation and why they should hire you.
- Include relevant references, but ask them for permission first and ensure their contact information is correct.
- Search job boards that post machine learning engineer jobs.
Start your journey toward becoming a machine learning engineer
Machine learning offers many opportunities for potential careers, and people in this field earn high wages and have a solid future. Now is a great time to start working toward a career as a machine learning engineer.
Find out what jobs would most interest you and what roles are available in your desired field, as well as what skills and experience are required. Consider using your skills to analyse the data and formulate a plan to launch your career as a machine learning engineer.
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