Curious what a career in machine learning will pay you in numbers? If you are weighing a machine learning engineer degree or a machine learning engineering course, money is naturally high on the list. Salaries in ML and AI look eye-popping compared with many other tech roles. But they vary widely globally.
Everywhere you go, you get new salary bracket offers by country, industry, company, and experience. In this article, I will cut through the noise with real-world figures, clear comparisons (including how much artificial intelligence engineers make vs. how much data scientists make), and practical advice so you can estimate what to expect at the entry level and as you climb the ladder.
What do Machine Learning engineers earn presently?
In the United States, the top search analysis results indicate to us that the average machine learning engineer salary sits in the high five-figures to low six-figures. Most of the credible aggregators report mid-to-high six-figure averages for senior roles. Entry-level roles are lower but still competitive compared to standard software roles. City, company (big tech vs. startup vs. finance), and total compensation (base, bonus, equity) shift these numbers dramatically. These machine learning tools can boost your income wallet up to 2 to 3 times your base salary on autopilot.
Entry-level machine learning engineer salary:
If you are just starting, the entry-level machine learning engineer salary in major markets typically ranges from roughly $60k–$100k in the U.S., depending on whether you are at a small company or a Big Tech firm. Remote roles, internships converted to full-time, and strong internship/project portfolios can push you to the upper end quickly. Outside the U.S., entry numbers are lower on paper but may have different purchasing power. For example, entry ML/AI roles in Pakistan and parts of Eastern Europe are lower in USD but remain competitive locally for all engineers. Suppose you have mastered these machine learning skills and have a lot of experience under your belt. You can easily fill your wallet. Always see the root, not the surface. Live your dream life with your own budget compensation.
Artificial Intelligence VS Machine Learning:
People use “AI engineer” and “machine learning engineer” interchangeably sometimes, but compensation patterns are similar. AI engineers working on applied systems, deep learning, or production ML pipelines often command salaries at or above those of Machine learning engineers focused on more classical models. In finance and hedge funds, AI roles can pull especially high pay rates in financial accounts (some public reports show offers well into the $300k–$400k range for senior specialists). Shortly, if you can focus on high-impact, productionized AI systems or domain-specific models (finance, ad tech, autonomous systems), expect premium pay.
Data scientists Vs Machine Learning engineers:
Typically, a data scientist is getting a bit lower salary bracket than Machine Learning engineers. But the overlap is large. Data scientists often focus on analysis, experiments, and business-facing models. Machine Learning engineers build scalable model infra and deploy models to production. Average data scientist salaries in the U.S. are commonly reported in the $120k–$150k range. At the same time, ML engineers tend to be higher as they take on software engineering responsibilities. If you can combine both skill sets, strong modelling and production engineering, you are in a powerful position to command top pay.
Global view of Machine Learning:
Salaries of ML have a huge difference by geography. The U.S. (especially the Bay Area and New York) and Switzerland top the charts, always followed by pay rates of Canada, Australia, and parts of Western Europe. Emerging tech hubs are catching up at the same rates, but the gap persists to certain limits.
For example, a senior Machine Learning engineer in the United States may earn 2 to 3 times the base of a counterpart in Eastern Europe or South Asia. But local cost of living and compensation structure (stock options, perks) change the practical value. If you are open to relocating or remote roles, you can easily arbitrage these differences. Companies increasingly hire remote talent at market rates, narrowing some gaps. Every country offers different pay rates according to the no of skills commands you have. Globally, we can calculate the average salary of machine learning engineers as given below across the world:
Country | Average annual salary offers |
United states | $155,000 to $177,000 |
Switzerland | $255,000 to $265,000 |
Iceland | $215,000 to $220,000 |
Canda | $115,000 to $125,000 |
Australia | $120,000 to $155,000 |
Germany | $90,000 to $110,000 |
South Africa | $15,000 to $20,000 |
| Mexico | $20,000 to $25,000 |
Impact of Machine Learning degree:
A machine learning engineer degree (MS/PhD) often speeds access to higher starting salaries and research/lead roles, but it’s not the only path. Bootcamps, an intense machine learning engineering course, practical projects, and demonstrable production experience can get you hired. Deployed models, reproducible experiments and coding chops are the premium things that employers are willing to pay for easily. Always try to invest in a well-known course, in a portfolio of real projects and a reputable course or certification to fill the gap.
Machine learning developer salary: is it different?
The term “machine learning developer salary” sometimes refers to engineers who implement machine learning models within software products. These roles typically fall between software engineers and ML research. If your job is engineering-heavy (APIs, model serving, MLOps), their role can be compensated with senior software engineering pay. If it is a research-heavy role, then it is perfectly matched to ML research roles. The takeaway for you is to clarify your thoughts for the role, whether it is a role of research, engineering, or hybrid, before using the job title as a proxy for salary.
Machine Learning Engineers Get High Perks from Industries:
Industry matters such as finance, big tech, and specialized startups (autonomous vehicles, robotics, ad tech) are willing to pay high rates. Finance firms and hedge funds sometimes pay skyrocketing cash bonuses, such as FAANG and top unicorns, balance base pay with equity that can outsize salary as time passes. In contrast, non-tech sectors like healthcare, academia, and government may offer lower pay but greater mission, providing you with alignment or stability. If you want maximum upside, target companies that monetize ML directly or where ML drives core product revenue annually, which you can see from their graphical analysis easily:
Beyond base salary:
Do not fix yourself on base pay alone. Total compensation includes equity, signing bonuses, performance bonuses, and benefits in healthcare, parental leave, and remote stipends. At startups, equity is the long-term goal that we consider first. The firms with strong potential always offer steady bonuses, and RSUs can be huge. They also provide non-monetary perks such as flexible hours, remote policy, learning budget, and mentorship. These affect career velocity and future earning potential. You should calculate an expected 3 to 5-year earnings picture when comparing offers. You can take a view of their yearly earning from every country they offer, as per the year of your experience:
| No of experience years | Annual Salary offer |
| 0 to 1 | $ 98,965 |
| 1 to 3 | $112,427 |
| 4 to 6 | $123,156 |
| 7 to 9 | $167,489 |
Practical Tips to Boost Your Base Salary:
Want to achieve big checks?
- Production projects, learn MLOps and systems engineering, publish or contribute to open-source, specialize in NLP, CV, and recommender systems that can add significant value to your profile.
- Deployment, scaling, and model monitoring in a realistic environment can step up your knowledge by considering an advanced machine learning engineering course or targeted certifications.
- Do networking as much as you can to make your online presence stronger, and to do intense interview prep and negotiate offers (ask for data) to make a measurable difference.
Salary grows when you move with strategy, not with emotions. Upgrading yourself every 2 years is a sign of growth rather than staying in one place.
FAQs:
Do ML engineers unlock more pay rates?
ML engineers unlock more pay rates easily because they have a strong grip on data, algorithms, and real-world impact. Companies do not just want these ML engineers , they actually need them. This demand pushes salaries upward.
Who gets a higher pay rate depending on their skills, an AI engineer or an ML engineer?
AI engineers get higher salary than ML engineers because they create robotics code , AI generative animations or deep learning due to their higher commands on the prompts.But the pay stamp is real check. So, AI engineers win due to their skillsets and make a huge impact over the ML engineers.
Is machine learning a high-paying job?
Absolutely, and not just hype. Machine learning is one of those rare careers where effort meets reward. If you invest time in learning, experimenting, and building real projects, the financial return can be massive. ML is high-paying because it saves companies time, money, and bad decisions. And businesses happily pay top dollar for that.
Are ML engineers paid more than software engineers (SWEs)?
Most of the time, yes.But with a catch. A general SWE may earn well, but ML engineers often earn more because they combine software engineering, data science, plus math. However, a top-tier SWE at a big tech firm can still out-earn an average ML engineer. Skill depth beats job titles every time.
How much does a junior machine learning engineer get when working remotely?
A junior ML engineer get paid remotely salaries range from $45,000 to $85,000 per year, depending on the company, country, and your portfolio. If you have strong projects in your bag, it is a golden ticket for you.
Conclusion:
Machine learning careers are a game-changer, but for the long term. When it all depends on your experience, location, industry, and whether you build production-ready systems or stay research-focused. If you have a machine learning engineer degree or a machine learning engineering course under your belt, then you must count on demonstrable projects and production experience to maximize your value.
Compare how much artificial intelligence engineers make with how much data scientists make, and do not forget total compensation, like their equity and bonuses. With the right skillsets and strategies, you can move the needle from base pay into a skyrocketing check pay bracket within a few years.
Reference:
https://www.statista.com/topics/9583/machine-learning/
https://www.statista.com/statistics/1449765/india-highest-paying-jobs-in-ai-and-ml/
