What Is an ML Engineer?
An ML engineer, or machine learning engineer, is a professional who builds and maintains scalable machine learning solutions in production. They are the bridge between the theoretical world of data science and the practical world of software applications.
An ML engineer's role involves several key responsibilities. They are tasked with designing and implementing machine learning models, a process that involves understanding business challenges, transforming data into actionable insights, and then coding and testing algorithms to solve these problems. They also work closely with data scientists to implement and deploy complex machine learning algorithms.
Not only are ML engineers responsible for the design and implementation of models, but they also maintain these systems. This can involve ensuring the performance, reliability, and scalability of these models. They also continuously monitor the performance of models, making necessary adjustments to ensure optimal performance and accuracy.
This is part of a series of articles about machine learning engineering.
In this article:
The Average Machine Learning Engineer Salary by Experience
According to salary surveys published in 2023, here is the range of salaries for machine learning engineers depending on experience:
- Entry-level ML Engineers in the U.S. can expect salaries ranging from $28,000 to $59,999 per year, reflecting the initial phase of their career and the learning curve associated with it.
- Mid-level ML Engineers, with several years of experience, can earn between $99,000 to $180,000 annually, signifying the value of their growing expertise and impact in the field.
- Senior ML Engineers, who have amassed extensive experience and expertise, have an average salary of around $155,211 per year, demonstrating the premium placed on their high-level skills and leadership capabilities.
Machine Learning Engineer Salaries Around The World
According to surveys published in the relevant countries as of 2023, here is what MLOps engineers can expect to earn around the world:
- United States: The general average salary ranges from $116,387 to $160,568 per year.
- Canada: In Canada, the average salary for a machine learning engineer is approximately CAD $113,000 per year, ranging from CAD $90,000 to CAD $196,000. Additional cash compensation averages around CAD $15,000 annually.
- India: ML Engineers have an average salary ranging from ₹810,462 to ₹12,00,000 annually. Additional cash compensation varies between ₹91,001 to ₹2,93,083.
- United Kingdom: The average salary for an ML Engineer is between £51,528 to £95,247 per year, with additional cash compensation ranging from £6,360 to £97,969.
- Australia: ML Engineers earn between AU$83,757 to $135,623 annually. Entry-level ML Engineers in Australia can expect an average total compensation of around AU$67,893.
- Germany: In Germany, a machine learning engineer can expect an average gross salary of €98,003 annually. However, according to Glassdoor, the national average is around €65,000 per year. The actual salary may vary based on location and company.
- Japan: The average annual salary for a machine learning engineer in Japan is JPY 10,830,085, with a range between JPY 7,451,098 and JPY 13,223,533. Salaries vary based on experience and qualifications.
- France: In Paris, France, the average salary for a machine learning engineer is about €48,726, while the gross salary averages €82,922 annually.
- Brazil: In Brazil, the average salary for a machine learning engineer is around R$162,000 per year, with potential bonuses bringing the total compensation up to R$184,000 annually.
- South Africa: Machine learning engineers in South Africa earn an average of R1,020,006 per year, with entry-level positions starting at R531,156 annually. The most experienced workers can make up to R9,600,000 per year.
- Spain: In Spain, the average annual salary for a machine learning engineer is €62,094, with entry-level positions starting at €27,252 and senior positions earning up to €417,864 annually. In Madrid, the average salary is around €68,136 per year.
- Italy: In Italy, machine learning engineers earn an average of €68,092 annually, with salaries ranging from €46,848 to €83,141. The national average salary is around €33,103.
- Mexico: The average salary for a machine learning professional in Mexico is MXN $441,000 per year, ranging from MXN $342,000 for entry-level positions to MXN $720,000 for more experienced roles.
Factors Influencing ML Engineer Salaries
The geographic location of an ML engineer plays a significant role in determining their salary. In regions with a high cost of living, such as major cities in the United States or Europe, salaries tend to be higher to compensate for the increased living expenses. For instance, ML engineers in San Francisco or New York City often earn more than their counterparts in smaller cities or towns.
Conversely, in countries or regions with a lower cost of living, salaries may be comparatively lower. However, it's important to consider the living standards and purchasing power in these areas. Additionally, the presence of technology hubs and the demand for ML talent in specific regions can also influence salary levels. For example, countries with a booming tech sector, like India, have seen a significant increase in salaries for ML engineers due to high demand and competition for skilled professionals.
Experience and Expertise
The level of experience and expertise is another crucial factor influencing an ML engineer's salary. As professionals gain more experience in the field, their salaries tend to increase, reflecting their enhanced skills and value to employers. Senior ML engineers with several years of experience, especially those who have led projects or teams, typically command higher salaries.
Expertise in specific areas of machine learning can also lead to higher earnings. For instance, ML engineers with advanced skills in deep learning, computer vision, natural language processing, or generative AI are often in higher demand and can negotiate better compensation. Additionally, those who continuously upgrade their skills and stay abreast of the latest developments in the field are more likely to secure higher-paying positions.
Industry and Sector
The industry or sector in which an ML engineer works is another significant factor that can affect their earnings. ML engineers employed in industries such as finance, healthcare, and technology tend to have higher salaries due to the high value placed on machine learning in these sectors. For instance, ML engineers working in financial services or tech giants like Google, Amazon, or Facebook are often among the highest earners in the field.
On the other hand, ML engineers working in academia, government, or non-profit sectors may experience lower salary ranges, as these sectors typically have tighter budget constraints. However, these roles can offer other forms of compensation, such as greater job security, more flexible work hours, or a better work-life balance. It's important for ML engineers to consider not only their salary but also the overall benefits and work environment when choosing a sector to work in.
Related content: Read our guide to machine learning workflow
Tips for Boosting Your ML Engineer Salary
Develop a Strong Foundation in Core Skills
As an ML engineer, developing a strong foundation in core skills is crucial to your success and your salary. Understanding machine learning algorithms, data analysis techniques, and programming languages such as Python or R can significantly increase your value to employers.
Moreover, having a good understanding of statistics and probability, data structures, and algorithms is equally important. These skills are often critical in developing efficient machine learning models and can set you apart from other candidates.
Specialize in High-Demand Areas
Specializing in high-demand areas can also boost your salary as an ML engineer. These areas change as the tech industry evolves, but currently, deep learning, natural language processing, reinforcement learning, and the emerging field of generative AI are particularly in-demand.
By specializing in these areas, you can position yourself as a valuable asset to your company and increase your earning potential. However, it's important to continually monitor the industry trends and adjust your specialization as needed.
Continuous Learning and Certification
In the rapidly evolving field of machine learning, continuous learning is crucial. New techniques, algorithms, and best practices are constantly emerging, and staying up-to-date with these changes can significantly impact your salary.
One way to demonstrate your commitment to continuous learning is through certification. Several reputable institutions offer machine learning certification programs. These certifications can validate your knowledge and skills, making you more attractive to employers and clients.
Gain Practical Experience
Finally, gaining practical experience is one of the most effective ways to boost your salary as an ML engineer. Theoretical knowledge is important, but being able to apply that knowledge to solve real-world problems is what truly sets you apart.
Seek out opportunities for practical experience, whether through your job, internships, or personal projects. This experience not only boosts your resume but also allows you to develop problem-solving skills that are critical in the machine learning field.
Stay Ahead of the ML Curve with Run:ai
In today’s highly competitive economy, enterprises are looking to Artificial Intelligence in general and Machine and Deep Learning in particular to transform big data into actionable insights that can help them better address their target audiences, improve their decision-making processes, and streamline their supply chains and production processes, to mention just a few of the many use cases out there. In order to stay ahead of the curve and capture the full value of ML, however, companies must strategically embrace MLOps.
Run:ai’s AI/ML virtualization platform is an important enabler for Machine Learning Operations teams. Focusing on deep learning neural network models that are particularly compute-intensive, Run:ai creates a pool of shared GPU and other compute resources that are provisioned dynamically to meet the needs of jobs in process. By abstracting workloads from the underlying infrastructure, organizations can embrace MLOps and allow data scientists to focus on models, while letting IT teams gain control and real-time visibility of compute resources across multiple sites, both on-premises and in the cloud.
See for yourself how Run:ai can operationalize your data science projects, accelerating their journey from research to production.