What Is an AI Engineer?
An AI engineer (artificial intelligence engineer), is a professional responsible for developing, testing, and implementing artificial intelligence models. They are the architects of the AI world, using their expertise in machine learning, deep learning, and AI algorithms to design AI systems that can perform tasks that would normally require human intelligence.
For example, an AI engineer might work on designing a system that can recognize human speech, create original images, or make predictions based on historical data. They might also be involved in developing the underlying infrastructure that supports these systems. This could involve anything from creating algorithms to developing software to managing cloud infrastructure.
This is part of a series of articles about machine learning engineering
In this article:
What’s the Average Artificial Intelligence Engineer Salary?
Now that we have a better understanding of what an AI engineer does, let's delve into the topic that you're probably most curious about: the AI engineer Salary. As a rapidly growing field, AI offers competitive salaries that reflect the high level of skills and expertise required.
According to recent studies, the average AI engineer salary globally is approximately $110,000 per annum. This figure can be significantly higher in developed countries such as the United States, where the average is around $140K per year. In contrast, in developing nations, the average salary might be relatively lower.
It's also worth noting that the AI engineer salary often comes with additional benefits, such as bonuses, stock options, and comprehensive health insurance packages. These additional perks can significantly increase the overall compensation package for AI engineers.
AI Engineer Salaries Around the World
Let’s take a global tour to get a sense of how the AI engineer Salary varies in different parts of the world. It's important to note that these figures are averages and can differ based on factors like experience, skills, and the cost of living in each country.
The figures are based on salary surveys published in late 2023 in each of the respective countries.
- Average annual salary: $145,898
- Annual salary range: $114,000 to $141,000
- Additional cash compensation: $23,660
- Average annual salary: CA$104,704
- Annual salary range: CA$80K - CA$125K
- Additional cash compensation: CA$3,014
- Average annual salary: £59,108
- Annual salary range: £43,000 to £75,000
- Additional cash compensation: £4,918
- Average salary in London: £66,391.
- Average annual salary: €92,388
- Additional cash compensation: €3,991
- Average annual salary: Around ₹800,000
- Annual salary range: ₹400,000 to ₹1,000,000
- Average annual salary: $116,000
- Annual salary range: $133,335 to $173,335
- Additional cash compensation: $20,000
- Average annual salary: €53,959 per year
- Average additional cash compensation: €5,112
- Average salary in Paris: €55,510 per year
- Average annual salary: ¥9,943,766
- Annual salary range: JP¥45L - JP¥64L.
- Average salary in Tokyo: JP¥62,87,193
- Average annual salary: S$74,943
- Annual salary range: S$90,000 - S$170,000
- Additional cash compensation: $6,400
- Average annual salary: R735,267 per year.
- Average salary in Johannesburg: $704,122 per year
- Average annual salary: €35,966 per year
- Additional cash compensation: €5,138
- Average salary in Barcelona: $36,067 per year
Factors Influencing AI Engineer Salaries
Geographic location plays a crucial role in determining AI engineer salaries. In regions with a high cost of living, such as major cities in the United States, Europe, and parts of Asia, AI engineer salaries tend to be higher to compensate for the increased expenses. For instance, salaries in Silicon Valley or New York City are significantly higher than the national average in the USA. This is due to the concentration of tech companies in these areas and the high cost of living. Conversely, in countries with lower living costs, salaries might be lower but still competitive in the local context.
The variation in salaries across different regions is also influenced by the local demand for AI expertise. In tech hubs like San Francisco, Berlin, or Bangalore, the demand for AI engineers is high, leading to competitive salaries. However, in regions where the tech industry is less developed, salaries may be lower, reflecting the local demand and economic conditions. Therefore, AI engineers willing to relocate or work remotely for companies in high-paying regions can potentially increase their earning potential.
Experience and Expertise
Entry-level AI engineers typically earn less than their more experienced counterparts. As AI engineers gain experience, they develop a deeper understanding of AI algorithms, data handling, and problem-solving skills, which are highly valued. Mid-level engineers with several years of experience can see a noticeable increase in their salaries, while senior engineers with a decade or more of experience can command top-tier salaries, reflecting their expertise and ability to handle complex projects.
Expertise in specific AI domains can also influence salary. For instance, AI engineers specializing in deep learning, computer vision, natural language processing, or generative AI often earn more due to the high demand and specialized knowledge required in these areas. Staying updated with industry trends and continuously upgrading skills in these high-demand areas can significantly impact an AI engineer's salary trajectory.
Industry and Sector
The industry or sector an AI engineer works in can greatly impact their salary. AI engineers working in high-profit sectors like finance, healthcare, or technology generally receive higher salaries. For example, AI roles in finance, where AI is used for risk assessment or algorithmic trading, can be particularly lucrative. Similarly, in healthcare, where AI is applied for diagnostics or personalized medicine, salaries are competitive to attract top talent.
Startups might offer lower base salaries but compensate with stock options or other incentives. In contrast, established tech giants can offer higher base salaries and comprehensive benefits packages. Government and academic roles in AI typically offer lower salaries compared to the private sector but might provide more stability and opportunities for research and development.
A strong educational background in computer science, data science, or a related field is often essential for a career in AI. Higher qualifications like a master's degree or Ph.D. in AI-related fields can lead to higher salaries. These degrees often involve advanced coursework and research in AI, machine learning, and data science, equipping graduates with in-depth knowledge and skills highly valued in the industry.
However, formal education is just one part of the equation. Continuous learning through online courses, workshops, and certifications in emerging AI technologies and tools can also positively impact salaries. Specializations in high-demand areas like reinforcement learning, ethical AI, or quantum computing, either through formal education or self-study, can set an AI engineer apart in the job market, leading to higher salary offers.
Related content: Read our guide to artificial intelligence cost estimation (coming soon)
Tips for Boosting Your AI Engineer Salary
Learn New Programming Languages and Tools
One of the most effective ways to boost your AI engineer salary is by learning new programming languages and tools. The AI field is constantly evolving, and new tools and technologies are being developed all the time.
By staying up-to-date with these developments and learning new languages and tools, you can make yourself more valuable to employers and command a higher salary. Some of the most sought-after languages and frameworks in the AI field include Python, Tensorflow, and PyTorch.
Develop Skills in Areas of High Demand
Another effective way to boost your AI engineer salary is by developing skills in areas of high demand. As mentioned earlier, skills in machine learning, deep learning, and natural language processing are highly sought after in today's market.
By developing these skills, you can increase your value to employers and command a higher salary. This can involve taking online courses, attending workshops, or even pursuing a higher degree in these areas.
Network and Build Connections
Networking and building connections can also help boost your AI engineer salary. By building a strong network, you can learn about new job opportunities, negotiate better salaries, and learn valuable insights from industry professionals.
Additionally, having a strong network can also provide you with mentors who can guide you in your career and help you make strategic decisions to boost your salary.
Certifications can play a significant role in enhancing an AI engineer's skill set and salary potential. Certifications from reputed organizations or technology companies in AI and machine learning validate an engineer's skills and knowledge in specific areas. For instance, certifications like TensorFlow Developer, Azure AI Engineer, or IBM AI Engineering can be highly beneficial. They provide evidence of proficiency and commitment to staying abreast of technological advancements.
When selecting certifications, it's important to focus on those that are most relevant to the engineer's career goals and the current industry trends. Certifications in areas like ethical AI, AI for edge devices, or cloud-based AI services are increasingly sought after. By strategically choosing certifications that are in high demand and align with their career path, AI engineers can enhance their marketability and justify higher salaries. Additionally, certifications often come with the added benefit of networking opportunities and access to professional communities, further aiding career development.
Stay Ahead of the AI Engineering 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 AI 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.