Machine Learning Engineer Hiring Costs: US Salaries vs. Hiring Offshore

Thinking about bringing AI capabilities to your business? You’re probably wondering exactly how much hiring a machine learning engineer is going to set you back.

The short answer: it depends largely on where you hire from.

For growing companies and startups, the difference between US-based and offshore ML engineer salaries isn’t just a line item on a budget—it’s often the deciding factor in whether you can afford to pursue AI initiatives at all.

In this article, we’ll examine ML engineer salary comparisons across regions, explore what goes into these costs, and highlight the main factors to take into account when negotiating salary.

How Much Does It Cost to Hire Machine Learning Engineers in the US? 

Machine learning is thriving, with businesses across industries integrating AI-driven solutions to stay competitive. The US, in particular, leads the global ML market. As a result, the demand for ML engineers remains high.

Here’s what you’re looking at for annual salaries of full-time ML engineers in the US:

• Junior to mid-level ML engineer: $146,000 to $174,900
• Senior ML engineer: $159,000 to $191,400

Keep in mind that these figures fluctuate by location. A machine learning engineer in San Francisco or New York might command a salary over the ranges above, while the same role in cities with lower costs of living, like Detroit or Cleveland, might come in at the lower end of the range. 

Salary is only part of the total hiring cost, of course. You must also factor in the following:

• Benefits: Health insurance, retirement contributions, stock options, and other perks
• Employer taxes: Social Security, Medicare, and unemployment taxes
• Recruitment costs: Job postings, recruiter fees, and hiring processes
• Onboarding and training: Resources for setting up and integrating new hires

So when you see that $146,000 starting salary, you’re actually looking at a total investment closer to $180,000 to $200,000 for the first year.

While many of these costs also apply when hiring offshore—you’ll still need to recruit, onboard, and provide some benefits regardless of location—the significantly lower base salaries in other regions often mean your total investment remains substantially lower than US-based hires.

That said, hiring in the US makes perfect sense for certain situations—when you need team members physically present for collaboration, when you’re working with sensitive data subject to US regulations, or when a deep understanding of the US market is crucial to your ML applications.

Comparison of ML Engineer Salaries by Region

Thanks to the normalization of remote work and cloud infrastructure, location is no longer a limiting factor for ML engineering talent. Companies can now look beyond their local markets without sacrificing quality.

When you examine the numbers across different regions, you’ll notice significant variations in compensation. These differences largely reflect local costs of living rather than disparities in skill levels or quality of work.

Let’s look at what ML engineers typically earn across different regions so you can get a clear picture of the global talent market:

‍Latin America

Latin America has emerged as a hot spot for ML talent, offering substantial cost savings while maintaining a significant overlap of working hours with the US. 

Here’s a breakdown of what you can expect to pay:

• Junior to mid-level ML engineer: $45,600 to $85,200
• Senior ML engineer: $85,200 to $100,800

That’s a 46% to 69% savings compared to US salary averages—and you get to collaborate in real-time with your team just like you would with US-based remote talent. No waiting overnight for answers or scheduling 6 a.m. meetings.

Cities like São Paulo, Mexico City, and Buenos Aires have developed vibrant tech ecosystems, with the region’s IT services market projected to hit $60 billion this year. These tech hubs are producing ML engineers who understand US business culture (as they’ve worked with companies like Google, IBM, Amazon, and Microsoft) and can seamlessly integrate with your existing teams.

When you weigh the value of time zone alignment against pure cost savings, many companies find that LatAm offers the most practical balance for their ML hiring needs.

Eastern Europe

The average salary of offshore ML engineers in top Eastern European countries varies little. In Poland, it ranges from $25,080 to $53,280, while the average salary in the Czech Republic ranges from $35,904 to $54,384.

Eastern Europe is also gaining recognition as a global tech hotspot. Poland, for instance, has attracted significant investments from tech giants such as Google, which is expanding its operations to enhance AI applications in energy and cybersecurity.

Meanwhile, Prague’s Charles University and the Czech Technical University produce strong technical talent, and the country’s AI ecosystem grows under the government’s National AI Strategy. 

Africa

ML engineers in top African countries like South Africa earn around $55,000, while those in Egypt earn around $26,000.

Africa’s tech landscape should not be overlooked. South Africa hosts subsidiaries of major tech companies like IBM, Cisco, Microsoft, and Amazon, solidifying its status as a global tech hub. 

Egypt’s tech scene is also thriving, supported by a growing number of startups and favorable government AI policies and strategies. These developments have made Africa an attractive location for sourcing ML talent.

Southeast Asia

Filipino ML engineers earn roughly $11,400, while Vietnamese ML engineers are estimated to make $12,600.

Southeast Asia is rapidly becoming a significant player in the global tech industry. Vietnam, for example, is positioned to become a global leader in high-impact sectors such as digital transformation and AI, with the fastest digital growth rate in the ASEAN region.

The Philippines is also a go-to offshoring destination with its large pool of tech talent proficient in English. These factors, combined with cost-effective salary structures, make the region ideal for hiring AI/ML experts.

South Asia

ML engineers in South Asian countries such as India typically earn $10,700 a year, while a Bangladeshi ML engineer makes around $8,100.

It’s well known that South Asia offers a large, skilled talent pool for offshoring. India’s tech industry has attracted substantial investments, with companies like Microsoft committing $3 billion to expand AI and cloud capacity.

Bangladesh is also making strides, hosting over 1,200 startups and numerous IT companies. The region’s tech-savvy population, coupled with competitive salary ranges, makes it an appealing destination for hiring ML engineers.

Things to Consider When Negotiating an ML Engineer’s Salary

When negotiating an ML engineer’s salary, there are several factors to consider beyond just the base pay. These factors ensure that you’re offering a fair and compelling package while also aligning with the unique needs of your company.

Below are some things to keep in mind during salary discussions:

• Market value: Further research the going rates for ML engineers in the region you intend to hire from. Knowing the market value helps you understand whether your offer is competitive and fair based on the engineer’s skills and experience.
• Experience level: An engineer with more years of experience or a proven track record can command a higher salary. Take into account their work history and the complexity of projects they’ve handled.
• Skill set: Focus on the key skills that are most important for your business needs. A mix of technical and soft skills is essential for smooth operations.
• Educational background: An ML engineer’s educational qualifications can influence their salary expectations. Engineers with advanced degrees or certifications may have a higher value in the job market.
• Job role and responsibilities: The specific nature of the ML engineer’s role within your team, be it project leadership, research, or development, will affect salary expectations.
• Geographic location: As we’ve seen, onshore and offshore hires have different salary norms. Understanding these differences helps you make a more informed decision, especially when hiring internationally.
• Total compensation package: This can vary by role type. Part-time and project-based roles may focus on hourly rates or project bonuses, while full-time positions might include health insurance and retirement contributions. The package should be tailored to the role.

When hiring internationally, ML engineer recruitment and staffing agencies can assist with salary negotiations. With local market knowledge, they can help you offer a fair salary to prospective candidates who are pre-vetted and ready to contribute to your team.

Final Thoughts

When it comes to hiring machine learning engineers, the decision between US-based and offshore talent involves more than just comparing salary numbers. Each region offers distinct advantages beyond cost savings that can significantly impact your team’s productivity.

Latin America stands out for its substantial overlap with US working hours, allowing for real-time collaboration and meetings during normal business hours. This makes LatAm an ideal choice when synchronous communication is important to your workflows.

Eastern Europe and Africa offer a middle ground with a few hours of overlap for East Coast companies, enabling some same-day interaction while still requiring some scheduling adjustments.

South Asia and Southeast Asia typically operate with minimal or no overlap with US working hours, meaning you’ll primarily rely on asynchronous communication and next-day responses. This can work well for certain projects but presents challenges when immediate collaboration is needed.

The right choice ultimately depends on your business priorities—whether that’s maximizing cost savings, ensuring real-time collaboration, accessing specialized expertise, or some combination of these factors.

Whatever direction you choose, having a clear understanding of the total costs involved and setting realistic expectations will help ensure a successful hiring process and a productive working relationship.

To learn more about the practical steps involved in finding and onboarding remote ML talent, check out our article on how to hire a remote machine learning engineer.