Why You Should Hire Machine Learning Engineers Offshore and How to Do It

With AI adoption accelerating across industries, the increasing demand for ML talent is making hiring in the US challenging.

That’s why more companies are turning to offshore hiring to find the ML talent they need. Whether you’re struggling with endless recruitment cycles or simply can’t afford US-level salaries for the expertise you need, this approach is your solution.

In this guide, we’ll walk through how to hire offshore machine learning engineers who can deliver the same quality work as their US counterparts—but at a fraction of the cost. We’ll cover where to find them, what skills to prioritize, and how to manage the entire process effectively.

Benefits of Hiring Machine Learning Engineers Offshore

Why should you consider offshore ML engineers instead of focusing solely on local talent? There are several compelling advantages:

Access to a larger talent pool

According to a recent McKinsey report on AI, over 60% of companies surveyed reported challenges in hiring ML engineers. Opening your search to international candidates dramatically expands your talent pool, alleviating some of the challenges. 

Instead of limiting yourself to ML specialists in your immediate geographic area or even just the US, you gain access to qualified professionals worldwide. This broader search increases your chances of finding the specialized skills and experience you need, especially for niche AI domains or complex implementation requirements.

Significant cost savings

US companies typically save 30–70% on salary costs when hiring offshore machine learning engineers compared to US-based talent.

For example, a machine learning engineer in the US might command an annual salary between $146,000 and $191,000, while professionals with similar qualifications in Latin America might earn $45,000 to $100,000. This difference comes from lower costs of living, not lower quality work.

These savings can be strategically reinvested to fuel growth in other areas of your business—whether that’s expanding your AI initiatives, increasing your marketing budget, hiring additional team members, or investing in new technologies.

Many companies use the capital saved through offshore hiring to fund initiatives that would otherwise be out of reach.

Time zone advantages

Depending on where you hire from, you can either have ML engineers working during your business hours (through hiring in Latin American countries) or create a follow-the-sun model where development continues after your local team has logged off (through hiring from Southeast Asia or Eastern Europe).

This flexibility allows you to either maintain real-time collaboration or accelerate development cycles for projects with tight deadlines. Machine learning projects often involve complex, iterative processes that benefit from either approach, depending on your specific needs.

Faster implementation of AI/ML initiatives

It’s an understatement to say the AI landscape is evolving rapidly. By leveraging offshore talent, you can move quickly on machine learning initiatives without the delays associated with extended domestic hiring processes.

This speed-to-implementation can provide a crucial competitive advantage, allowing you to operationalize machine learning models and start generating insights or automating processes sooner than competitors still struggling to build their in-house teams.

Where to Hire Offshore Machine Learning Engineers

When hiring offshore ML engineers, you need to consider both the geographical location and the hiring method that best suits your needs.

Choosing the right region

The offshore location you source ML talent from affects everything from salary expectations to working hours compatibility.

Here are the main regions to consider:

Latin America 

Latin American countries like Argentina, Brazil, Mexico, and Colombia offer a compelling nearshore option for US companies. The term “nearshore” refers to outsourcing to countries that are geographically close, which provides several advantages:

• Similar time zones allow for real-time collaboration
• Cultural similarities to the US facilitate easier communication
• High levels of English proficiency, especially among professionals

Hiring in Latin America also offers: 

• A large pool of talent with experience working with US companies
• Lower salary expectations than US-based talent

Tech hubs in cities like Buenos Aires, São Paulo, Mexico City, and Bogotá have thriving AI and ML communities. Mexico, for example, has seen rapid investment growth in its AI sector, attracting $640 million over the past two years, with machine learning making up 23% of the market.

Brazil’s University of São Paulo is ranked #1 in Latin America for machine learning and 85th in the world, producing highly skilled graduates who fuel the region’s growing ML sector.

Southeast Asia

Countries like India and the Philippines have long been popular for offshore hiring due to:

• Large pools of tech talent
• Very competitive rates
• High English proficiency
• Established outsourcing infrastructure

India has the world’s largest pool of STEM graduates, creating a massive talent pipeline that fuels a thriving IT market while keeping costs competitive. Indian IT firms have extensive experience with AI and ML solutions, making this a well-established offshoring destination.

The main challenge is the significant time zone difference, which can complicate real-time collaboration. However, this time difference can actually be advantageous for certain ML projects, creating a “follow-the-sun” workflow where development and model training can continue 24/7.

Eastern Europe

Countries like Ukraine, Poland, and Romania offer:

• Strong technical education systems with an AI focus
• Moderate time zone differences (more manageable than Southeast Asia)
• Competitive rates (though typically higher than Southeast Asia)
• Strong English proficiency

For example, Poland has emerged as a European tech powerhouse with over 60,000 technology companies and a strong educational system producing highly skilled AI/ML specialists. Polish engineers are known for their technical precision and ability to handle complex AI-driven projects.

Hiring methods 

Once you know where in the world you want to hire from, you need to decide where you will find the talent. Most people go with one of these options:

Freelance platforms

If you’re looking to dip your toes into offshoring, marketplace platforms like Upwork, Toptal, and Fiverr might seem tempting. They give you immediate access to ML talent globally and handle payment logistics.

These platforms work well when:

• You need a specialized ML expert for a one-off project
• You’re testing the waters with a specific AI implementation
• You need to move quickly without a lengthy hiring process

The downside? You’ll spend significant time sifting through candidates of wildly varying quality. Many ML engineers on these platforms have impressive profiles but limited practical experience deploying models in production environments.

And while the hourly rates might look reasonable at first glance, those costs add up quickly for complex ML projects that inevitably take longer than expected.

Job boards & LinkedIn

Some companies prefer handling the entire process themselves through international job boards and LinkedIn. This approach gives you complete control over your candidate search and eliminates middleman fees.

LinkedIn can be particularly powerful because:

• You can proactively headhunt passive candidates (who aren’t actively job searching)
• The platform shows mutual connections who might provide informal references
• Advanced search filters let you target specific ML skills, frameworks, and experience levels

But be prepared—posting an ML engineer role on LinkedIn or global job boards like Indeed often unleashes a tidal wave of applications.

Without a structured evaluation process, you’ll waste countless hours reviewing unqualified candidates. And once you find promising engineers, you’re still on the hook for handling all the cross-border hiring complexities yourself.

Recruitment and staffing agencies

For most companies, working with a specialized agency that focuses on offshore hiring for ML roles is the most efficient path. 

The best partners offer:

• Direct access to hidden talent pools: They’ve already built networks in tech hotspots, connecting you with ML engineers you simply couldn’t find on your own.
• Real technical screening: They go beyond keyword matching to truly evaluate candidates’ ML capabilities through practical assessments and technical interviews.
• Rapid hiring timelines: They can connect you with a qualified ML engineer in just weeks, compared to the 3-6 months it typically takes companies hiring on their own—getting your AI initiatives up and running faster.
• Cultural compatibility assessment: They vet for the communication skills and work styles that determine whether someone will thrive as a remote team member, not just their technical abilities.
• Complete cross-border logistics: They handle the contractual, payment, and compliance complexities that often derail DIY international hiring.

While this approach has associated fees, the time savings and quality assurance often deliver far more value than the DIY alternatives—especially for companies without existing connections in offshore tech communities.

Essential Skills to Look for in Offshore Machine Learning Engineers

When hiring offshore ML engineers, you need to evaluate both technical abilities and soft skills that enable effective remote collaboration. But here’s the reality: too many companies focus exclusively on technical skills and then wonder why their offshore relationship isn’t working smoothly.

Think of it this way—a machine learning engineer might be brilliant with complex algorithms and neural networks, but if they can’t translate their work into clear explanations or communicate challenges effectively, you’ll end up with models that don’t solve your actual business problems.

The most successful offshore hires are those who combine strong technical foundations with the communication skills and work habits that allow them to integrate seamlessly with your in-house team, despite the physical distance.

Hard skills (technical must-haves)

These are the technical skills offshore ML engineers should possess:

• Programming proficiency: Strong Python skills are essential, along with experience in ML frameworks like TensorFlow, PyTorch, and Scikit-learn. Depending on your projects, knowledge of R, Java, or C++ may also be valuable.
• Algorithm expertise: Understanding of various ML algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning architectures.
• Math and statistics knowledge: Solid foundation in linear algebra, calculus, probability, and statistics—the mathematical building blocks of machine learning.
• Data processing skills: Experience with data preprocessing, feature engineering, and handling large datasets using tools like Pandas, NumPy, and PySpark.
• Cloud platform experience: Familiarity with cloud-based ML services like AWS SageMaker, GCP Vertex AI, or Amazon S3.

Soft skills

Technical skills alone aren’t enough for successful offshore arrangements. Look for these equally important qualities:

• Strong communication: Can they explain complex ML concepts clearly to non-technical stakeholders? This is crucial for remote collaboration.
• Self-direction: The ability to work independently, manage time effectively, and stay productive without constant supervision.
• Problem-solving mindset: ML projects often encounter unexpected challenges. Look for engineers who can troubleshoot creatively and persistently.
• Cultural adaptability: Will they integrate well with your team’s working style and understand your business expectations?
• Attention to detail: Machine learning requires precision in everything from data handling to model tuning to documentation.

Education background

While not always essential, a degree in computer science, mathematics, physics, or a related field (especially when paired with relevant experience) provides a strong foundation for ML work. 

How to Hire an Offshore Machine Learning Engineer Directly: 8-Step Process

If you decide to manage the hiring process yourself, follow these steps to find the right ML engineer:

1. Get realistic about your budget

Let’s talk money first. Before diving into the offshore ML talent pool, understand what you’re working with:

• Research what ML engineers actually cost in your target regions
• Don’t just look at averages—understand what the top 1–10% command (these are the people you want)
• Factor in additional costs like equipment, software licenses, and onboarding resources

While offshore hiring absolutely delivers cost savings, don’t fall into the “cheapest is best” trap.

Quality ML engineers offshore will still command respectable salaries relative to their local markets. The real win comes from getting exceptional talent at reasonable rates, not bargain-basement hiring.

2. Get crystal clear on what you need

Hiring a machine learning engineer without specifying exactly what you need is like ordering “food” at a restaurant—you’ll get something, but it probably won’t be what you wanted.

Create a detailed job spec that includes:

• The specific ML domains you work in (NLP, computer vision, recommender systems, etc.)
• Required frameworks and languages (TensorFlow, PyTorch, Python, etc.)
• Experience with particular model types relevant to your projects
• Production deployment experience (crucial if you need models in real-world use)
• Any industry knowledge that’s non-negotiable (finance, healthcare, etc.)

Don’t just list technologies—explain the actual problems your ML engineer will solve. If you’re building a recommendation engine to increase e-commerce sales, say that. It attracts engineers who want to work on meaningful challenges, not just check technical boxes.

3. Cast a smart net

Now it’s time to get your opportunity in front of the right people. Where you promote your role matters as much as how you describe it:

• Use regional tech job boards in your target markets, not just global platforms
• Leverage LinkedIn with geo-targeted job promotions and direct outreach
• Consider ML-specific communities like Kaggle, GitHub, and AI research groups
• Tap your existing team’s networks—they might know the perfect candidate

4. Develop an evaluation system that actually works

Once applications roll in, you need an effective filtering system:

• Create a scoring rubric based on must-have vs. nice-to-have skills
• Look for evidence of completed projects, not just listed technologies
• Pay attention to communication quality even in initial applications
• Check for contributions to open-source ML projects or research papers

Remember that an ML engineer who can explain complex concepts clearly in writing is far more valuable than one with an impressive but incomprehensible resume. Clear communication is a non-negotiable for remote work success.

5. Test practical ML skills, not theoretical knowledge

Technical assessments for ML engineers should mimic real-world problems, not academic exercises:

• Have candidates work with messy, real-world data (not pristine datasets)
• Ask them to explain their approach and decisions, not just deliver a model
• Include model deployment challenges, not just model building
• Test for ability to handle constraints like limited compute resources or data privacy requirements

The goal isn’t just to see if they can build a model—it’s to see if they can solve business problems using machine learning in practical settings.

6. Interview for remote collaboration skills, not just technical chops

Your interviews should evaluate candidates on two equally important dimensions:

• Technical depth: Probe their ML experience with specific, scenario-based questions tailored to your business needs
• Remote effectiveness: Assess communication style, problem-solving approach, and self-management skills

Here are a few of our recruiters’ favorite interview questions for ML engineering candidates:

• “How do you keep up with advancements in AI/ML?” (Shows engagement with research papers, conferences, and emerging trends.) 
• “What types of ML models have you worked with? Can you describe a project where you built and deployed an ML model?” (Tests hands-on experience and ability to communicate complex technical work clearly.) 
• “How do you handle situations where stakeholders don’t fully understand the capabilities or limitations of AI?” (Evaluates communication skills and ability to set realistic expectations.) 
• “What aspect of ML/AI excites you the most? Why?” (Helps assess genuine passion and alignment with your company’s projects—candidates should demonstrate authentic interest, not just salary motivation.)

Watch for candidates who respond with concrete examples rather than theoretical answers, acknowledge limitations honestly, and demonstrate genuine enthusiasm for collaborative problem-solving. 

7. Make a competitive offer

When you’ve found your ideal ML engineer, move decisively:

• Present an offer that’s competitive for their local market (not just the lowest you can get away with)
• Be clear about working hours, availability requirements, and communication expectations
• Decide on contractor vs. employee status and explain the implications
• Use reliable global payment methods (or a partner like Deel or an EoR to handle this)
• Ensure your IP protection and data security requirements are clearly addressed

The best talent will have multiple options, so don’t drag out the process or try to negotiate aggressively. A fair, transparent offer demonstrates respect and sets the relationship up for success.

8. Onboard with intention

Your offshore ML engineer’s success hinges on their first few weeks with your company:

• Create a 30/60/90 day plan with clear goals and expectations
• Provide access to all necessary data, systems, and resources from day one
• Schedule regular check-ins to troubleshoot issues early
• Pair them with an existing team member who can provide context and connections
• Invest time in explaining not just what you’re building but why

The best onboarding approaches give new ML engineers both technical foundation and business context. They need to understand your data infrastructure, but they also need to understand your customers’ needs and how machine learning creates business value for your company.

How to Hire a Machine Learning Engineer Through a Staffing Partner

Working with a staffing partner who specializes in offshore or nearshore hiring can dramatically simplify the hiring process. (We’re clearly biased here since this is a service we offer—but that just means we know it works. We see our clients returning to make additional hires after experiencing the benefits firsthand.)

The process will vary depending on the staffing partner you choose, but here’s how it typically works (based on our process):

1. Requirements definition: The partner spends time understanding not just technical requirements but also your company culture and project goals.
2. Candidate matching: Within days, you receive profiles of pre-vetted candidates who match your specific ML needs.
3. Streamlined interviews: You interview only candidates who have already passed technical assessments, focusing on project-specific questions and team fit.
4. Offer and onboarding support: The partner manages and supports offer logistics and provides onboarding guidance.
5. Ongoing support: Quality partners check in regularly to ensure successful integration. They also handle payroll disbursements and local compliance logistics.

Final Thoughts

Hiring offshore machine learning engineers can transform how your company implements AI initiatives while significantly reducing costs. Whether you choose to partner with a specialized recruitment firm or manage the process yourself, the key is being methodical and deliberate in your approach.

The most successful offshore hiring arrangements are those where companies view their international team members as valuable extensions of their core team—not just as cost-saving resources.

By focusing on finding ML engineers with the right combination of technical skills, communication abilities, and cultural fit, you can build an AI team that delivers powerful solutions regardless of geographic location.

If you’d like to skip the complexities of sourcing, vetting, and hiring offshore machine learning engineers on your own, book a free consultation call to discuss how we can help you find top Latin American ML talent that aligns perfectly with your needs. Our pre-vetted candidates can help you start implementing ML solutions within weeks.