Machine Learning Engineer: Key Roles and Core Skills Explained
The development of AI and ML technologies has brought a number of new promising professions to the labor market. In this article, we will tell you about one of them. You will learn what machine learning engineers do, what skills they have, what tools they use, how much they earn, and what an ML-engineer can expect in terms of career growth.
What is a Machine Learning Engineer
A machine learning engineer is a highly skilled IT professional who researches, creates, and implements software to automate artificial intelligence (AI) and machine learning (ML) models. Their primary focus is developing AI and ML algorithms that can self-learn and predict. Such programmers typically work in a data science team, collaborating with other engineers, data analysts and architects, administrators, DevOps experts, and more.
ML engineers develop large-scale systems to process large volumes of data and use it to train algorithms. They then configure and deploy the software, making it available to end users. These specialists manage all stages of data processing, including its discovery and preparation, creation and training of models, and their implementation in business processes.
Key tasks of a machine learning engineer:
- Evaluation, analysis, and systematization of large volumes of data.
- Conducting tests and optimization of ML and AI algorithms.
- Deploying, scaling, updating, and retraining existing models.
The profession of an artificial intelligence and machine learning engineer includes a considerable list of job responsibilities:
- Selecting the best method for preparing data sets for analysis and analyzing them.
- Loading source data into machine learning models.
- Design, development, and research of AI and ML systems.
- Studying, transforming, and converting data science prototypes.
- Selecting suitable data sets for collection and modeling.
- Conducting statistical analysis to improve models.
- Training and retraining of ML algorithms.
- Preparing infrastructure for model deployment.
- Data visualization for further research.
- Maintenance, updating, scaling, and management of existing models.
- Identifying differences in data distribution that may affect model performance.
- Analysis of ML algorithm application options and their ranking.
- Extending existing ML frameworks and libraries.
- Checking the quality of data and then cleaning it.
- Communicating with clients to explain the capabilities of ML models and obtain information about business requirements.
- Deploying models to production: first as a prototype and then through an API to end users.
- Technical support for other data and product specialists.
An ML engineer is often confused with a data scientist, believing that they are the same thing. To clarify, here is a brief comparison of machine learning engineer vs data scientist. These professions are indeed similar in many ways, as both require an almost identical set of skills, are associated with the use of similar technologies, and the processing of large amounts of data. At the same time, there are significant differences between them.
ML engineers design and develop tools and infrastructure for the implementation and scaling of AI and ML systems in production, as well as in other business processes. Data scientists have a slightly different vector of activity. They collect, analyze, and interpret large volumes of data to develop hypotheses and forecasts, analyze trends for individual companies and the market as a whole. It is worth noting that representatives of both professions make a significant contribution to the development of progressive information technologies.
Core Skills Required
Those interested in how to become a machine learning engineer should know that successful mastery of this specialty will require a combination of the skills of a software engineer and a data specialist. Applicants should understand the fundamental concepts of computer science and data analysis, be proficient in mathematics and statistics, and have knowledge of machine learning and neural networks. In addition, they will need analytical skills and soft skills such as problem-solving, communication, and teamwork.
The most in-demand machine learning engineer skills are:
- Linux/Unix. To work with cluster data and servers, you need to have a good command of the specified operating systems.
- Python, Java, C, C++. These languages are most often used in data analysis and preparation for ML algorithms.
- GPU and CUDA programming. Graphics processors optimize the process of developing large-scale ML models. The CUDA interface is commonly used to work with GPUs and deep learning models.
- Modeling and data evaluation. ML engineers must be able to evaluate large amounts of data, as well as plan and model it.
- Spark and Hadoop. These technologies process large-scale data sets to solve machine learning problems.
- Applied mathematics. To work effectively, specialists need strong mathematical knowledge. They need to be especially well-versed in linear algebra, probability theory, statistics, multidimensional calculations, tensors and matrix multiplication, algorithms, and optimization.
- Neural network architecture. Knowledge in this area allows using a set of algorithms to train ML systems and perform complex cognitive tasks.
- Natural Language Processing (NLP). This technology enables AI and ML algorithms to perform a range of text generation, processing, and analysis tasks. The most in-demand skills include Word2vec, recurrent neural networks (RNN), Gensim, and the Natural Language Toolkit (NLTK).
- Reinforcement learning. The ability to work with a set of algorithms gives a specialist the ability to teach neural networks complex tasks based on repeated experience.
- Distributed computing. The use of this technology locally and in the cloud helps in working with large amounts of data.
Tools and Technologies
Now that you know how to become a machine learning engineer, it is worth talking about the tools and technologies used in this field. The programming languages Python, Java, C, and C++ mentioned in the previous section are the basis for this profession. In addition to them, ML engineers use a number of services, libraries, and frameworks.
Main tools:
- TensorFlow. This is a popular open-source library for developing ML and DL (deep learning) models, developed by the Google Brain team. It contains a solid base of resources for numerical calculations. Developers can efficiently train and deploy systems through the high-level Keras API.
- PyTorch. Based on the Torch library, this open-source platform with Python and C++ interfaces allows you to create ML models of various types, including for natural language processing and computer vision. Many well-known projects in the field of machine learning were created with its help, including PyTorch Lightning, Hugging Face's Transformers, and Tesla Autopilot.
- Google Cloud ML Engine is a managed cloud platform with tools for developing, training, predictive modeling, and running ML models of any size with any type of data.
- Amazon Machine Learning (AML). The popular ML platform allows you to build mathematical models, make predictions (batch or real-time), and identify patterns. It integrates data from different sources (Redshift, Amazon S3, RDS), and supports the export and import of models.
- Accord.NET. Knowledge of this framework is one of the common machine learning engineer requirements. The machine learning platform developed on the basis of .NET is often used for scientific calculations. It provides libraries for ML systems of different types, including pattern recognition, linear algebra, and statistical data processing.
Career Outlook
According to a study by 365 Data Science, the projected demand for AI and ML specialists will grow by 40% by 2027. Indeed's job search engine included this profession in the top 10 most in-demand specialties of 2023, based on the growth dynamics of vacancies related to AI and ML. However, not all of them concern experienced professionals. Employers apply for junior specialists no less actively than for middle and senior machine learning engineers.
The income of a machine learning engineer varies depending on a number of parameters: location, experience, industry, and more. According to a number of subject resources (Payscale, ZipRecruiter, Salary.com, Glassdoor), the average annual salary of an ML engineer in the US fluctuates between $112,832 and $143,180.
The Coursera portal provides more detailed statistics:
- The average salary for a junior machine learning engineer with 1 year of experience is $127,350 per year.
- The average salary for a middle-level specialist with 4–6 years of experience is $150,193 per year.
- The average salary for a senior machine learning engineer with 10 years of experience is $162,356 per year.
In addition to experience, the location of companies willing to hire such an employee is important. For example, the highest salaries for ML engineers are offered in San Francisco and New York — $182,854 and $188,926 per year, respectively. The lowest average annual salary is received in Boston ($163,799) and Florida ($161,578).
Challenges Faced by Machine Learning Engineers
If, after reading this far, you have firmly decided to master the profession of an AI and machine learning engineer, tempted by a high salary, do not rush. Before making a final decision, familiarize yourself with the challenges that its representatives face.
The main difficulties include:
- Lack of training data. The efficiency of ML models directly depends on the data loaded into them. Their lack leads to a drop in the performance of algorithms and a deterioration in the results they generate.
- Low-quality data. Low-quality data does not allow for creating a full-fledged, highly effective neural network. In order to create a high-quality product, machine learning specialists are forced to spend most of their time selecting, organizing, and cleaning information.
- Overfitting data. One of the most difficult challenges for ML engineers is developing an overly complex machine learning model and trying to fit it to a limited data set.
- Irrelevant features. Enough quality data is not sufficient to develop a top-notch machine learning model. The wrong features can ruin the product and render it worthless.
- Security. The AI and ML industry regularly faces security challenges. These include not only external cyber threats but also the risk of data leakage due to human factors. An equally serious challenge is fake data. Its use in products can lead to very unfavorable consequences.
Conclusion
Machine learning engineer is one of the most promising and in-demand professions in 2024. This specialist is responsible for the development and configuration of machine and deep learning models, as well as their deployment and implementation in the business processes of client companies. The most priority areas of application of AI and ML technologies include natural language processing (NLP), computer vision, software development with artificial intelligence, and more.
According to experts, the machine learning market is expected to grow rapidly in the near future. According to calculations by analysts at Fortune Business Insights, the global ML industry will grow from $26.03 billion in 2023 to $225.91 billion by 2030. This means a steady increase in the number of vacancies and machine learning engineer salary, which makes the profession very attractive for both beginners and experienced IT specialists.