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Machine Learning Engineers create computer programs that enable machines to take actions without being specifically directed to perform those tasks. This job combines computer programming and data science to enable systems to learn and improve from experience automatically by using machine learning, a subset of artificial intelligence. Machine Learning Engineers work in a variety of industries including tech, finance, manufacturing, education, IT, automotive, and even fast food.
Machine Learning Engineers can find full-time positions both remote and onsite. They typically work a standard 40-hour week but will occasionally work late if something goes wrong. Each machine learning role will be different depending on the company the engineer is working for, but you can usually find them: attending standup meetings, monitoring models, writing algorithms, performing analyses, collaborating with their team, writing unit tests, reviewing code and pull requests from colleagues, cleaning data, or managing resources.
Machine Learning Engineers combine data science skills with software development knowledge. They need to strongly understand data science concepts, such as algorithms, math, data structure, probability and statistics, data modeling and evaluation, libraries, programming languages used for data science, and visualizing data. They should also be proficient in software development concepts, like computer architecture, system design, and coding languages, and know how to write clean, maintainable software and systems. Machine Learning Engineers need to have a grasp on their analytical skills for projecting outcomes and troubleshooting issues.
Machine Learning Engineers are expected to know technologies, like Jupyter Notebook, Amazon Web Services (AWS), APIs; version control such as Git; and programming languages, like Python, R, Java, C++, and SQL. Not all of these languages are required for an entry-level position, but it is necessary to know two or more of these languages.
Machine Learning Engineers should have a high-level understanding of how all of these technologies and languages do and don’t work together so that they can make the best technical choices for their specific employer and their business requirements. Additionally, being able to create stable and scalable models is of the utmost importance. MLEs are also often responsible for the distribution of resources within their department, including hardware, data, and personnel.
The world of machine learning is changing both rapidly and dramatically so Machine Learning Engineers must have a willingness and even love of learning in order to keep up with industry evolutions. A keen eye with strong attention to detail also strongly benefits a Machine Learning Engineer. Good communication skills are necessary for this position to explain machine learning concepts to non-technical coworkers and stakeholders. Basic writing skills are a plus for this position, as some machine learning positions require Engineers to publish articles or reports on their work.
Data science combines domain knowledge, programming skills, mathematics, and statistics to infer crucial insights from data. These insights can be used by businesses, governments, and any other data-collecting entities to inform decisions.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It is used to write scripts, automations, algorithms, manipulate data, and create frameworks. Python prioritizes simplicity, easy to learn syntax, readability, and versatility.
SQL stands for Structured Query Language. It is a computer language used to store, manipulate, and retrieve data which is stored in a relational database.
Machine learning is the use and study of computer algorithms that improve automatically through experience. It is a subset of artificial intelligence (AI). Machine learning is used in everything from email filtering to Netflix recommendations.
R is a programming language and free integrated development environment (IDE) for statistical computing and graphics. R is most commonly used by Data Scientist and Statisticians for developing statistical software and data analysis.
Mathematics are used on a day-to-day basis by many technical positions. Subjects like linear algebra, calculus, statistics, and probability are used by Data Scientists, Cybersecurity professionals, Developers, Motion Graphics Designers, Engineers, and more.
A Machine Learning Engineer in the United States makes, on average, $120,311 annually, according to Indeed.com.
Salaries for Machine Learning Engineers vary by region within the the United States. Listed below are some Machine Learning Engineer salaries for specific areas with the United States compared with the average national salary:
Machine Learning Engineers are expected to have a two- or four-year degree in mathematics or computer science. However, colleges offer minimal artificial intelligence courses at the undergraduate level, and employers are noting a deficit in the number of qualified machine learning professionals. Some companies accept candidates without a degree who have demonstrated proficiency or who have attended a machine learning-focused coding bootcamp. For positions beyond entry-level, many employers expect candidates to have a master’s or doctoral degree.
Machine Learning Engineers can enjoy remote and onsite full-time employment opportunities. They can find positions in many industries from government to tech, fast food to automotive. While the majority of these roles are onsite, remote employment is rapidly becoming more popular.
Machine Learning Engineers can find jobs on these sites:
Machine Learning Engineers can find remote jobs on these sites:
Currently, employers are noting that there are more open machine learning positions than there are candidates with relevant experience and degrees. Don’t be afraid to apply for positions for which you have the skills but not all of the qualifications. Employers are looking for experience, a demonstrated understanding of theory, and a college education. If you can excel at a minimum of two of those three qualifiers, you’ll stand out in the pool of applicants.
To gain experience: work on personal projects, participate in hackathons and coding challenges, and contribute to open-source projects. Document everything possible, either through GitHub or a blog. Communication is key in this industry; often, mishaps in machine learning are actually miscommunications between the client and the engineer, or a misunderstanding on the clients’ part about what they think machine learning is capable of. It’s your job to explain what and how you can do the job for them with machine learning in a way that is not too technical for them to understand. If communication is not one of your strengths, practice!
Consider writing and sharing blogs, as this will: give employers insight into your thought processes, allow them to get to know you better, and showcase your writing skills applicable to the job. Putting your name out there and demonstrating these skills when you share blogs will alleviate the pressure on interviewing and attract attention from potential employers.
You can also practice explaining technical concepts about projects you’re working on, to family, friends, or people who were in your class or cohort. Consider going to those people to work on your interview skills as well. mock-interviews are the most efficient way to ace the interview process and quell nerves. There are also online mock-interview services if you don’t have anyone willing to work with you on these.
If you’re still having trouble finding a job, begin by examining different machine learning specialties and focusing more deeply on only one. Commonly scouted specializations include: computer vision, recurrent networks, reinforcement learning, natural language processing, generative adversarial networks, meta-learning, one-shot learning, and neural network visualization and debugging. You don’t have to choose your entire career path’s specialization now, but it helps to find a specific niche that excites you and keeps your interest piqued. Do a few projects within specialization and see if you like it. Then, seek out jobs that are looking for that focus.
Machine Learning Engineers work in a variety of industries including tech, finance, manufacturing, education, IT, and automotive. Some employers will abbreviate machine learning to ML and you might find positions under that abbreviation on job boards. At some companies, machine learning professionals will have very specific niches and these will be specified within the job description but almost never in the title itself.
Machine Learning Engineers can look for these job titles:
Machine Learning Engineer is one of the most lucrative and technically demanding data positions. To work your way up to a Machine Learning Engineer role, you can try positions such as Data Analyst, Data Engineer, or Data Scientist. A Data Engineer role would be the most comparable to a Machine Learning Engineer. Data Engineers create the infrastructure for data and format data into a useful system, which Data Scientists use to analyze large amounts of data.
Data Analyst is the entry-level role in data. It is the lowest pay grade for this career path, but it is a fantastic place to start. A Data Analyst is responsible for collecting, processing, and analyzing data. They usually translate these numbers into actionable insights that help their employer make better business decisions. Data Scientist is also a great role to pivot from to become a Machine Learning Engineer. Data Scientists interpret the results of data that they’ve processed, modeled, and analyzed to create actionable plans for their employers.
Each of these roles requires some education in math, computer science, or both. These roles usually require a four-year degree, but some will accept a coding bootcamp certificate as a qualification. There are no standard certifications in this career path, but most vendors do offer certifications for databases. These certifications can help boost your application, but a strong resume is more important.
Data scientists collect, organize, and analyze large sets of data, providing analysis that is key to decision making. Governments, non-profits, and businesses of all types rely on data for forecasting, risk management, and resource allocation. Data scientists discover and analyze trends in data, and report their findings to stakeholders. They will use algorithms and models to simplify and mine data sets to create data-driven recommendations. Data scientists are needed across a handful of industries, especially the ubiquity of data and the reliance on it for business decision-making.Learn about becoming a Data Scientist
Data analysts review large amounts of data to summarize, analyze, and visualize it and provide insights. Working from data from multiple, relevant sources, they create and maintain databases, and use statistical techniques to analyze the collected data. Data analysts must be able to communicate with others about what the data shows and to be able to provide realistic recommendations based on their analysis. Many industries such as healthcare, advertising, and retail rely on the work of data analysts to inform their business decisions and strategy.Learn about becoming a Data Analyst
Data Engineers create the infrastructure for data and format data into a useful system which Data Scientists use to analyze large amounts of data. Data Engineers can specialize in pipelines, databases or platforms, warehouses or infrastructure, or be generalists.Learn about becoming a Data Engineer