According to the recent Future of Jobs Report by the World Economic Forum, AI and machine learning specialists will be key roles for business transformation over the 2025-2030 period. The field is expected to grow by over 80%, ranking as the 3rd fastest-growing job group.
What this means is that machine learning engineer skills are in high demand. Companies are now prioritizing the acquisition of ML talent as a strategic advantage. And this trend makes hiring professionals with ML skills highly competitive across all types of companies.
But what skills are essential for a machine learning engineer specifically? Let’s take a further look.
If you’re wondering about the full scope of responsibilities, check out our ML engineer job description guide for detailed role expectations at different seniority levels.
Let’s start with the technical skills that form the foundation of a machine learning engineer’s work.
First of all, every machine learning engineer must master at least one programming language. The choice of language often depends on the specific use case and existing technology stack within your organization.
Most often, machine learning engineer skills include the following languages:
Python is the dominant language in ML engineering due to its extensive ecosystem of libraries and frameworks. Most ML engineers use Python programming skills for data manipulation and model development.
R remains valuable for statistical analysis and data visualization, particularly in research-intensive environments. Java is useful for enterprise applications, while Scala is designed for working with big data platforms. C++ is relevant for developing custom algorithms that require low-level optimization.
A skilled machine learning engineer typically knows Python as their primary language, plus one additional language based on their specialization. Similar to software engineers, they should write clean, maintainable code that follows best practices for version control and documentation.
Mathematics and statistics are top skills needed for a machine learning engineer, since any model is built on these principles.
When an ML engineer trains a neural network or algorithm, the system performs matrix calculations, along with derivatives and probabilities. These steps allow the model to “learn” based on data.
Without understanding these processes, a machine learning engineer cannot explain why a model gives a specific result and how to improve it.
Statistics are no less important, as they help to work with data even before building a model. ML engineers use statistics to study distributions, detect patterns, measure error, and separate noise from real signals. Those methods also provide the basis for evaluating model quality.
In other words, without a mathematical mindset, a machine learning engineer will only be able to use pre-built libraries, but not analyze their work in depth.
Moving forward, machine learning relies on several types of algorithms, including:
Machine learning engineers need to be familiar with these approaches, as they cover the majority of practical tasks.
They should understand the assumptions made by each algorithm and the data it processes best. For example, regression works well with numerical relationships, while decision trees are effective in categorical data and nonlinear patterns.
A key part of the ML engineer’s job is selecting the right algorithm for the task. To achieve this, they should understand the algorithm’s mechanics: how it learns, which parameters affect its behavior, and how those changes influence the results.
Now let’s focus on the tools that accelerate the development of machine learning solutions.
TensorFlow and PyTorch are the preferred choices as primary deep learning frameworks for most organizations. A skilled ML engineer can build, train, and deploy neural networks using either platform.
In fact, research-focused teams prefer PyTorch for its flexibility and debugging capabilities, while production-oriented ones favor TensorFlow for its deployment ecosystem.
Additionally, you can find scikit-learn as part of classical machine learning engineer skills. It’s a widely used library that provides efficient implementations of regression, classification, clustering, and dimensionality reduction.
Beyond basics, there are some other ML frameworks that serve specific use cases:
The ML developer should be comfortable learning new frameworks as needed and not be locked into a single tool.
Before any model training begins, machine learning engineers spend considerable time preparing data. And data preprocessing and feature engineering skills help specialists determine what data the model works with and how useful it will be for training.
Preprocessing includes data cleaning, handling missing values, scaling numerical variables, encoding categorical features, and balancing datasets. ML engineers usually use Python libraries, like Pandas, NumPy, or the aforementioned Scikit-learn, for these tasks.
Feature engineering means creating new features or transforming existing ones to better reflect patterns. In this case, they also use Pandas and scikit-learn, and sometimes the feature-engine library as well.
These machine learning engineer skills matter because data quality often impacts results even more than algorithm choice. Good preprocessing and well-designed features can improve model performance and reduce training time.
For more insights on data capabilities and how they relate to ML engineering, you can read our guide on data engineer skills and tools.
Next, ML engineers need to work confidently with AWS, Google Cloud, or Microsoft Azure as primary environments where machine learning models run.
Amazon Web Services leads in ML cloud services with SageMaker for model development. It also provides EC2 for custom training environments and Lambda for serverless inference.
Google Cloud Platform offers strong integration with TensorFlow through Vertex AI and AutoML for automated model development. Azure provides similar capabilities through Azure Machine Learning Studio.
A good machine learning engineer should know how to manage cloud resources using infrastructure-as-code tools and monitor system performance through cloud-native solutions.
Model evaluation skills ensure engineers can systematically measure and validate model performance beyond basic accuracy metrics. This competency demonstrates understanding of statistical validation and the ability to make reliable deployment decisions based on performance analysis.
Middle+ ML engineers must be familiar with these evaluation metrics:
Additionally, advanced evaluation necessitates the implementation of A/B testing methodologies and statistical significance testing. And ML engineers need practical experience with monitoring systems and bias detection techniques.
Software engineering practices have finally reached machine learning, and MLOps represents one of the fastest-growing skill areas you need to evaluate.
As we mentioned earlier, your ML engineer should treat models like software and implement proper version control, testing, and deployment practices.
Git remains the foundation for code versioning. But unlike traditional software, ML systems involve three moving parts: code, data, and models.
Therefore, ML engineers can also utilize tools such as MLflow, Weights & Biases, and DVC (Data Version Control) to manage all aspects, in addition to the standard Git. These platforms track which dataset version produced which model, making it easier to reproduce results.
The next part of the machine learning developer skills list is an understanding of continuous integration and deployment (CI/CD), which automates model training and deployment processes.
Additionally, your ML engineer should be familiar with setting up automated testing. For these purposes, you can look for experience with tools like Jenkins, GitHub Actions, or GitLab CI.
Beyond the core technical competencies, machine learning engineer skills now include several specialized areas in domain-specific applications.
Below, we’ve grouped these additional skills, detailing their practical applications and how ML engineers develop expertise in each area. Let’s take a further look.
Technical expertise alone doesn’t guarantee success in ML engineering roles. The most important machine learning engineer skills often include must-have traits, such as continuous learning, problem-solving, effective communication, and critical thinking. Let’s take a look.
As we previously discussed at the outset, machine learning evolves faster than most fields in technology. New frameworks and modeling techniques appear nearly every year, and tools that once dominated quickly lose relevance.
An ML engineer must continually update their knowledge. They should read research papers, explore new versions of libraries, engage with professionals, and apply new methods. Without continuous learning, even the strongest technical machine learning engineer skills become outdated.
Real ML projects rarely follow a clean path. Datasets often include missing values, errors, biases, or noisy features that make off-the-shelf methods ineffective. Sometimes, even hardware limitations dictate which models can be used.
This is where problem-solving skills come into play. The ML engineer analyzes the problem, explores several solutions, experiments with different models and approaches, and tailors them to the specific context. Sometimes, it’s even about being able to simplify the task when necessary, rather than overthinking each step.
Machine learning engineers work at the intersection of multiple teams: data scientists, product managers, business analysts, and software engineers. Thus, they often must explain what a model does, its limitations, and what the results mean. Good communication skills reduce misunderstandings between teams and save time.
Models can give a false sense of success. They may show high accuracy on test data, but fail once deployed. The cause could be biased training data or even the wrong evaluation metric.
Critical thinking is a part of machine learning engineer skills that helps them question results. Do these datasets really represent the real world? Is this metric aligned with business goals? Is this model unnecessarily complex for the problem we’re solving? By asking these questions, ML engineers avoid costly mistakes and ensure models deliver reliable outcomes.
Verifying the skills required for machine learning engineer roles involves a combination of:
Here are a few approaches to confirm these skills.
Hands-on testing is the most reliable method for measuring machine learning skills. First, you can use coding platforms or in-house tests to check Python and SQL proficiency, data preprocessing, basic model training, or debugging.
The second way to check machine learning engineer skills is to implement take-home projects that mirror your actual work challenges. Provide real (anonymized) company data and ask candidates to build end-to-end solutions within a specified timeframe.
You can also look for candidates with some of these certifications:
But remember, certifications can support evaluation, but they should always be combined with hands-on testing.
Review candidates’ GitHub repositories to assess code quality and documentation standards. Look for complete projects with clear README files, proper version control usage, and well-structured code.
Examine their contributions to open-source ML projects or participation in the Kaggle community. During the interview, you can request detailed project walkthroughs, where candidates should explain their technical decisions and the challenges they faced.
Another popular method for testing machine learning engineer skills is behavioral interviewing. Here, you need to create scenarios that mirror your actual work environment. For example, you can present situations involving conflicting technical requirements or tight deadlines. Listen for structured approaches to problem-solving and realistic assessment of trade-offs.
Use specific behavioral questions about past experiences. Ask for concrete examples of difficult projects or learning new technologies under pressure. If your ML role involves collaborating with different departments, ask candidates to explain ML concepts to non-technical specialists.
Also, we recommend arranging technical discussions with your current ML engineers. Sometimes, candidates reveal more about their expertise and working style in conversations than in formal interviews.
All in all, companies will continue to increase their investment in machine learning skills, as a competitive advantage now depends more on data-driven digital transformation.
Yet, evaluating the right skills remains a challenge. McKinsey’s analysis shows that over 60% of organizations still struggle to hire qualified machine learning engineers, only about 10% lower than in 2022 or 2023.
If you want access to the right ML talent without draining internal resources, DOIT Software can help. Our talent network comprises over 715 top ML engineers across the USA, Eastern Europe, and Latin America. Each candidate can pass a tailored set of technical and behavioural interviews aligned with your requirements.
Just share your needs with DOIT talent matchers, and get vetted machine learning engineer skills for your team in as little as 5 business days.
A machine learning engineer builds, deploys, and maintains ML systems. They transform data science prototypes into production-ready applications, spending most of their time handling data preprocessing, implementing algorithms, and optimizing models.
Yes, machine learning engineers code using Python, SQL, Java, Scala, and other programming languages, depending on the business needs. Most often, they write scripts for data processing, implement ML algorithms, build APIs for model serving, and create deployment pipelines. So, coding represents a fundamental skill that ML engineers use nearly every day.
Absolutely. SQL frequently appears in machine learning engineer job postings because most business data lives in relational databases. ML engineers use SQL to extract training data, create feature sets, validate data quality, and integrate with data warehouses. They write complex queries to process datasets according to business requirements.
