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

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.

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:

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:

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. 

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.