The Most In-Demand Machine Learning Engineer Skills in 2024 [Research on 1,000 Job Postings]

Once upon a time, "Data Scientist" was the buzzword in the tech industry. But as technology continues to evolve, artificial intelligence (AI) and machine learning (ML) have taken center stage, and a new hot job title has emerged: Machine Learning Engineer.

In an age of giant strides in artificial intelligence, the need for machine learning engineering skills is skyrocketing. This increasing demand is not just creating more job opportunities but sparking a wave of innovation in technology and methodology within the field.

To grasp the current trends in the machine learning engineering job market, we've examined 1,000 job postings to identify the skills employers demand.

This article explores the accelerated growth of machine learning careers and the emerging opportunities it presents in this constantly transforming industry.

Key Findings

• The U.S. Bureau of Labor Statistics predicts a 23% growth rate for the machine learning engineering field from 2022 to 2032.
• The most in-demand programming languages for ML engineers include Python (56.3%), SQL (26.1%), and Java (21.1%).
• The leading open-source libraries for deep learning are PyTorch (39.8%) and TensorFlow (37.5%).
• The top cloud platforms include Microsoft Azure (17.6%) and AWS (15.9%).
• Employers seek multifaceted professionals with additional data engineering, architecture, and analysis skills.
• Approximately 12% of the machine learning engineer job postings specify their location as remote.
• Around 35% of the job postings openly state the anticipated salary range.
• According to Glassdoor, the average salary for machine learning engineers in 2024 will be \$166,000—a \$35,000 increase since 2023.
• Only around 28% of job ads explicitly state a requirement for a certain number of years of experience.
• Mid-level employees are most in demand, with 2-4 years of experience appearing most frequently in job postings.

Table of Contents

• Methodology
• What Does a Machine Learning Engineer Do?
• How Can You Become a Machine Learning Engineer?
• What Technical Skills Do You Need as a Machine Learning Engineer?
• Where Do Machine Learning Engineers Work?
• How Much Do Machine Learning Engineers Make?
• What Is the Machine Learning Job Outlook in 2024?
• Starting a Machine Learning Engineering Career
• FAQs

Methodology

Our thorough analysis examines 1,000 job advertisements for machine-learning engineering positions sourced from Indeed.

We began by collecting job postings from Indeed, covering various roles across diverse sectors and locations. We then singled out keywords associated with machine learning engineering positions from the job postings.

We verified the dataset's accuracy and relevance through careful data preprocessing, eventually narrowing it down to 1,144 pertinent postings. The graphs and tables in this article are based on these relevant postings.

We aim to highlight crucial patterns, qualifications, and characteristics of the present machine learning engineer job market, focusing on opportunities within the US.

What Does a Machine Learning Engineer Do?

The list of data-related roles is extensive and can often be confusing because of the similar terminology. This might leave you asking, "What exactly is a machine learning engineer?"

Machine learning engineering plays a pivotal role within the tech industry. It focuses on creating and implementing algorithms that enable machines to learn from and make database decisions.

Machine learning engineering skills involve developing intelligent systems that automatically learn and improve from experience without being explicitly programmed. These systems form the core of artificial intelligence applications.

Consider the following overview of the machine learning engineer job description:

• Design and implement machine learning models using suitable algorithms for different tasks.
• Clean data and address inconsistencies for robust model building.
• Train and adjust models with large datasets for improved accuracy and efficiency.
• Evaluate model performance using metrics and optimize results.
• Deploy models into production, ensuring scalability and performance maintenance.
• Monitor systems to ensure model performance on new data.

Machine learning engineers are vital to transforming theoretical data science models into practical applications that automate tasks, enhance decision-making, and unlock new insights from data. Their role is critical in enabling organizations to leverage advanced analytics and AI—driving innovation and efficiency in today's data-driven world.

How Can You Become a Machine Learning Engineer?

Machine learning engineering is a specialized role requiring a unique combination of data science, software engineering, and statistical analysis skills.

A machine learning engineer's career path typically involves an educational background in data engineering or computer science. The tech sector, however, emphasizes capabilities and skills—suggesting that you don’t necessarily need a machine learning engineer degree to fulfill job requirements.

As anticipated, engineering, computer science, and machine learning degrees are the most commonly sought-after qualifications, appearing in over 50% of the job postings.

Degrees less directly related but still prevalent in over 15% of the job postings include general subjects like statistics and mathematics, as well as specialized fields like data science and AI.

Surprisingly, degrees unrelated to the field—like architecture—were also noted. Architecture, appearing in 16.8% of job postings, may be valued for its emphasis on designing scalable, efficient systems. Architecture degrees also surfaced in our other job market research for 2024, including fields like data science, data engineering, and data analysis.

Other non-traditional degrees in the job postings include physics, economics, finance, information systems, and chemistry.

Considering this, it’s clear that even without a degree in engineering or computer science, you can still carve out a career in machine learning engineering. Many skills can be transferred across various roles—easing the transition from one area to another.

By building your specialized machine learning engineering skills through online courses, projects, and bootcamps, you can still gain the necessary background to thrive in the field.

But what are the typical education requirements for machine learning engineering roles?

The educational landscape for machine learning engineering roles is less cut and dried than you might think.

Let's break it down.

Around 25% of job postings ask for a bachelor's degree, and a similar portion ask for a master's degree. Interestingly, there's a slight lean towards PhDs, with about 30% of job postings highlighting it as a preference. But the spread is fairly even across these educational levels.

What does this mean for you?

It shows that higher education does have value in the machine learning engineering job market. A master's or PhD can give you a leg up in the job market, though bachelor's degrees remain highly valued.

But here's where it gets even more interesting: around 20% of the job postings didn't specify a degree requirement.

This isn't to say that these employers are not seeking degree holders. Instead, it hints at the possibility of breaking into the field even if your educational journey didn't follow the traditional path.

The tech industry focuses on what you can do, not just what degree you hold. So, with the right mix of determination, systematic learning, and a standout portfolio, you can make your way in the machine learning engineering job market—regardless of your academic background.

The 365 Data Science platform equips you with a broad spectrum of skills vital for a thriving career as a machine learning specialist. Plus, it allows you to work on hands-on projects that can showcase your talents to potential employers. No matter your current level of expertise, our platform is a place for you to hone the industry’s in-demand skills.

What Technical Skills Do You Need as a Machine Learning Engineer?

Machine learning engineers require a comprehensive set of technical skills to create, train, and deploy efficient, scalable machine learning models. They ensure that intelligent systems are effectively optimized for predictive analytics, automation, and artificial intelligence applications.

Consider the following machine learning engineering skills we found in our data.

Programming Skills

Programming languages are required for a career in machine learning to code algorithms, manage data, and implement models that enable machines to learn and make decisions.

Critical programming languages mentioned in our machine learning engineer job listings include the following.

Python (77.4%): Python dominates thanks to its extensive libraries for data analysis, machine learning, and deep learning. It enables the development of sophisticated algorithms and the processing of large datasets—making it an indispensable requirement for machine learning engineers.

SQL (26.1%): Essential for managing and querying data stored in relational databases, SQL is crucial for data preparation and feature engineering in machine learning workflows.

Java (22.8%) and Scala (8.5%): These languages are critical in environments with big data processing frameworks like Apache Hadoop and Apache Spark. These are vital for handling the volume and velocity of data typical in machine learning.

R (20.5%): While traditionally more common in statistical analysis, R is also a valuable machine learning skill for developing algorithms and exploring data—given its powerful packages for statistical computation.

JavaScript (5.4%): Though less prevalent, JavaScript is used in machine learning to develop interactive front-ends for models and visualizations and integrate machine learning models into web environments.

AI Skills and Tools

Machine learning is a subset of AI that involves algorithms learning from data to make predictions or decisions without being explicitly programmed. Accordingly, AI skills are integral to the role of a machine learning engineer.

Unsurprisingly, machine learning is a crucial skill for ML engineers, explicitly mentioned in 88.3% of job postings. But it's safe to assume that all ML engineer roles require machine learning skills. A general mention of AI follows this in 60.4% of postings.

Other significant AI skills include deep learning (24.8%), natural language processing (NLP) (21.4%), and knowledge of large language models (LLMs) (15.0%).

Deep learning engineers design, develop and optimize neural network models for various AI applications, ensuring their performance and integration into larger systems. NLP, on the other hand, is a branch of AI that enables computers to understand, interpret, and respond to human language.

Finally, LLMs (an application of NLP) are advanced AI systems trained on vast amounts of text data to understand and generate human-like language for various applications.

ML engineers utilize various specialized frameworks and libraries to work within these subfields of AI. These tools are critical requirements for machine learning engineers, offering a range of functionalities from deep learning and neural network development to more traditional machine learning processes.

• PyTorch (39.8%) and TensorFlow (37.5%): These are leading open-source libraries for deep learning. TensorFlow is known for its flexible and comprehensive ecosystem, while PyTorch is favored for its simplicity and use in research settings.
• scikit-learn (12.8%): scikit-learn is a popular tool for traditional machine learning tasks like classification, regression, and clustering. Its simplicity and wide array of supported algorithms make it essential for many predictive modeling tasks.
• Keras (10.6%): Often used in conjunction with Tensorflow, Keras is known for its user-friendly interface, which simplifies the creation of deep learning models. It's used for fast prototyping and runs seamlessly on both CPU and GPU.

Machine Learning Algorithms and Methods

As you might expect, the basis of ML engineering is specific machine learning skills and algorithms, which can be grouped into the following categories.

ML Fundamentals

Deep Learning: This subset tops the chart with 34.7% of job postings—highlighting the demand for deep learning engineers and their importance in handling complex data-driven tasks like image and speech recognition.

Natural Language Processing (NLP): NLP appears in 21.4% of postings—underscoring its significance in applications that require understanding and generating human language.

Computer Vision: In 20.3% of postings, computer vision indicates its relevance in fields like automated inspection, augmented reality, and various forms of image analysis.

Optimization: Noted in 19.0% of postings, optimization emphasizes the need for machine learning engineers to refine algorithms and systems for better performance.

Artificial Neural Networks (ANN) and Reinforcement Learning: These concepts are critical for creating adaptive learning systems—showing up in 8.8% and 8.3% of postings, respectively.

Model Development and Feature Engineering

Feature Engineering: Cited in 6.4% of postings, feature engineering is a vital machine learning engineering skill for improving model accuracy by transforming raw data into a format better suited for machine learning.

Predictive Modeling and Bayesian Methods: These techniques—appearing in 1.6% and 2.2%, respectively—are crucial for forecasting and statistical inference, enabling machines to make informed decisions based on data.

Specialized Machine Learning Techniques

Anomaly Detection and Clustering: These unsupervised methods are essential for identifying unusual data patterns and grouping similar data—mentioned in 2.7% and 3.5% of job postings, respectively.

Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): These networks are essential for processing sequential data and image data—appearing in 2.5% and 1.5% of postings.

Generative Adversarial Networks (GANs) and Decision Trees: These concepts highlight the need for expertise in specific types of neural networks and decision-making processes—appearing in 1.0% and 1.2% of postings.

Cloud Skills

Cloud skills are indispensable requirements for machine learning engineers. They allow professionals to leverage scalable computing resources to handle the intensive computational demands of training and deploying machine learning models.

Cloud platforms also provide services and tools that facilitate model collaboration, versioning, and deployment, allowing ML engineers to bring machine learning solutions to market efficiently.

Our research findings emphasize the importance of cloud technology in machine learning engineering.

• Top Platforms: Microsoft Azure (17.6%) and AWS (15.9%) are the most in-demand, recognized for their extensive and robust services that facilitate scalable ML deployments and comprehensive project management.
• Containerization Essentials: While not cloud providers themselves, Docker and Kubernetes are critical. Docker (15.9%) enables consistent development, shipment, and runtime environments for ML applications, while Kubernetes (16.3%) ensures efficient container orchestration and management.
• Specialized Services: Google Cloud Platform (8.2%) is valued for integrating with TensorFlow and other specialized ML tools, making it a key player in cutting-edge ML model development.
• Niche Usage: Although only mentioned in 0.9% of postings, Salesforce is relevant in CRM systems where ML enhances data analysis and customer interactions.

Data Engineering Skills

Our 2024 market research reveals a trend toward the need for multifaceted skill sets. In addition to traditional machine learning engineer skills, employers are seeking a "jack-of-all-trades" in data who can manage all aspects of the data timeline.

Smaller companies typically have fewer resources, so a small team handles all data-related tasks. On the other hand, larger companies can provide more specialized roles.

We observed similar trends in our research on the machine learning engineer market.

A few data engineering skills and software have been incorporated into the machine learning engineer requirements, and many of them appear prominently in the postings.

As evident with programming languages, SQL (26.1%) is essential for querying and managing data within relational databases—critical for data preparation in machine learning.

Azure (19.1%) and Docker (15.9%) are essential for their cloud computing capabilities and containerization, while Apache Spark (16.3%) and Data Pipelines (16.1%) are crucial for handling big data (13.5%) processing.

Data Architecture Skills

As illustrated in the graph below, data architecture skills overlap with data engineering.

Once again, SQL (26.1%) tops the list, followed by cloud computing (7.2%) and data modeling (6.3%)—the process of creating diagrams and structures that define how data is connected, stored, and processed within a system.

Data Analysis Skills

Although less significant than data engineering and architecture, data analysis skills also appeared in our ML engineer job postings.

Data mining (5.2%) aids in uncovering patterns within large datasets for model development, while statistical analysis (4.0%) ensures models are built on robust and statistically significant insights.

Data visualization is mentioned in 5.2% of postings. More specific tools are also noted, including:

• Tableau (4.5%)
• PowerBI (1.9%)
• Matplotlib (1.7%)
• Seaborn (0.9%)
• Looker (0.8%)

Although only mentioned in about 6% of machine learning engineer job postings, Microsoft Office tools are valuable skills for machine learning engineers because they enable them to document methodologies, report on model performance, and present findings effectively to stakeholders and non-technical team members.

The graph above illustrates the ratio of job listings that mention specific Microsoft Office skills. With a 56.3% share, Excel is a vital tool for manipulating data and conducting initial analyses. Meanwhile, Word (18.5%) and PowerPoint (7.4%) are beneficial for showcasing findings.

Where Do Machine Learning Engineers Work?

Our machine learning engineer job market research for 2024 consisted of 1,144 job postings originating from 718 unique companies across 41 US states.

California leads in the job offerings at 29.3%, followed by Washington, Massachusetts, Texas, New York, Virginia, Illinois, Pennsylvania, and Georgia in the top 10.

It’s also interesting to note that about 12% of the postings expressly set their location as remote.

Remote work expands opportunities in the machine learning career and allows companies to focus on skills rather than location, enhancing team diversity. But the lessened importance of location potentially increases competition because companies can source talent globally.

It's also worth noting that 16.43% did not specify a location—suggesting that even more remote opportunities could be available.

In terms of specific companies, we saw a few significant players in the machine learning engineer job market. Apple leads the way, with 3.7% of postings, followed by other well-known giants like Meta, TikTok, Tesla, and Amazon.

We also categorized job postings by employment type, including full-time, part-time, internship, or other positions.

Our findings show that full-time roles are the most common, making up 59.2% of the postings. All other roles constitute a small fraction of the dataset, with contract work at 2.1% and the remaining types below 0.5%. The rest of the postings did not specify an employment type.

How Much Do Machine Learning Engineers Make?

According to Glassdoor, the average machine learning engineer salary in the US for 2024 is \$166,000 annually, ranging from \$126,000 to \$221,000. Our research agrees that the most commonly cited salaries fall between \$160,000 and \$200,000. Interestingly, about 35% of the job postings we reviewed openly stated the anticipated salary range.

If you’re just breaking into the career, you may be curious about the entry-level machine learning engineer salaries. Below are Glassdoor's estimates of the total annual pay for the role in 2024, broken down by experience level.

• 0–1 Years: \$92K–\$166K/yr.
• 1–3 Years: \$111K–\$195K/yr.
• 4–6 Years: \$131K–\$229K/yr.
• 7–9 Years: \$148K–\$254K/yr.
• 10–14 Years: \$163K–\$284K/yr.
• 15+ Years: \$175K–\$307K/yr.

Only around 28% of job ads explicitly state a requirement for a certain number of years of experience.

Two to four years of experience were required in approximately 9% of total job postings, with four to six years being the next most common requirement in approximately 8.2% of postings.

The smallest group (3%) consisted of entry-level roles asking for up to two years of experience—indicating that most businesses prefer ML engineers with more experience.

But there's no need to be disheartened. The lack of experience requirements in over two-thirds of the job postings might indicate that the machine learning engineer job market is receptive to newcomers.

It’s also important to remember that this data specifically pertains to the job outlook for ML engineers in the US. Salaries can vary significantly based on numerous factors, including location, the size of the company, and the industry sector.

What Is the Machine Learning Engineer Job Outlook in 2024?

With all the news about tech layoffs and AI, you might wonder whether machine learning engineers are in demand in 2024.

Currently, we’re seeing an increasing availability of data and the integration of AI across various industries like healthcare, finance, and entertainment. Accordingly, AI jobs are in high demand.

ML engineers are necessary to develop algorithms that enable businesses to predict behaviors, optimize operations, and innovate products. Advances in computational power, cloud computing, and efficient AI models further heighten their necessity.

Machine learning engineers provide essential technical expertise and empower companies to remain competitive in a rapidly evolving digital landscape. This makes them crucial for implementing new AI-driven strategies and maintaining a technology edge.

The U.S. Bureau of Labor Statistics predicts that the number of jobs for computer and information research scientists—including the machine learning engineering career—will increase by 23% from 2022 to 2032. This growth rate significantly surpasses the average for all occupations, positioning ML engineering as one of the most rapidly expanding professions.

The increasing salaries of machine learning engineers highlight the importance of these roles in the tech industry. According to Glassdoor, the average ML engineer salary was approximately \$131,000 per year in January 2023. A comparison to 2024 shows that the average wage has risen by over \$35,000 within just one year. Given these trends, the machine learning job outlook seems promising.

Starting a Career in Machine Learning Engineering

Machine learning engineers are integral in today's data-driven environment. They’re at the helm of the digital revolution—empowering businesses to forecast trends, enhance operational efficiency, and pioneer new products, thereby securing their competitive edge in a constantly advancing technological sphere.

Our research revealed the most in-demand machine learning engineering skills. Proficiency in programming languages like Python, SQL, and Java is essential—complemented by an adeptness in AI tools and a deep understanding of machine learning algorithms. Skills in cloud computing are indispensable thanks to the substantial computational power required to train and deploy ML models.

While not a traditional role requirement, expertise in data engineering, architecture, and analysis can also give you a competitive edge in the field. They aid in data handling, manipulation, and interpretation—fundamental aspects of the machine learning workflow.

Educational machine learning engineer requirements vary. While advanced degrees like a master's or PhD are frequently preferred, many job postings also welcome candidates with just a bachelor's degree. Notably, approximately 20% of postings do not specify a degree requirement—indicating a shift towards valuing practical skills and experience over formal education.

This trend towards skills-focused hiring underscores that developing a solid machine learning skill set and a compelling portfolio is as crucial as formal qualifications. A blend of determination, structured learning, and practical experience can pave the way for success in this field, irrespective of educational background.

Prospective ML engineers should broaden their abilities, keep pace with emerging technologies, and showcase their practical expertise. The first step is to find the most beneficial learning resources.

The 365 platform offers a variety of courses that span a wide array of skills—from the essentials of ML to LLMs and NLPs. It also provides projects that allow you to demonstrate your competence to prospective employers. Regardless of your current level, adopting a continuous learning mindset can help you build your machine learning engineering skills and gain an edge in this competitive field.

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