How to Hire a Nearshore Machine Learning Engineer (Top Benefits, Countries, and Companies)

Machine learning (ML) is all the buzz right now, and for good reason. It’s one of the driving forces behind AI advancements, allowing systems to learn from data and make decisions without explicit programming. 

While many see ML as a new frontier, it has been around since the days of Alan Turing. Yet, despite its long history, many US businesses struggle to find local ML talent.

The demand for skilled ML engineers far exceeds supply, making nearshore hiring an attractive solution. Companies can access top-tier talent in Latin America and benefit from cost-effective rates. 

This guide covers the best LatAm countries for hiring nearshore machine learning engineers, key qualifications to look for, and top nearshore companies that can help.

Why Hire a Nearshore Machine Learning Engineer?

Hiring machine learning engineers is a strategic move for US companies struggling to find top talent locally. Instead of competing for scarce (and expensive) ML professionals in the US or dealing with the headaches of offshoring to distant time zones, nearshoring gives you the best of both worlds. It means hiring skilled professionals from nearby countries—primarily Latin America (LatAm) for US businesses—where you can save 30–70% on salary costs while working with engineers in the same or close time zones.

For US companies, this approach opens up access to LatAm’s growing pool of AI and ML talent, where engineers often have strong technical skills, excellent English proficiency, and cultural compatibility that makes collaboration seamless.

You’re not just getting more cost-effective engineering salaries—you’re tapping into the talent pools in countries where technology education and innovation are thriving.

Where to Find Nearshore Machine Learning Engineers

While ML talent can be found throughout LatAm, these countries stand out.

1. Mexico

Mexico has emerged as a leading nearshore destination for machine learning engineers. Tech hubs in Mexico City and Guadalajara produce highly skilled professionals, many of whom have worked on AI projects for US companies. 

The country’s AI sector has seen rapid investment growth, attracting $640 million over the past two years, with machine learning making up 23% of the market. Mexican universities also have strong ML and AI programs, maintaining a steady supply of talent.

2. Argentina

Argentina is home to some of Latin America’s most prestigious universities, including UBA and ITBA, which have strong AI research programs. The country has a well-established outsourcing industry that has given many tech professionals experience working with US projects. 

Argentina’s skilled workforce, combined with its strong research background, makes it an attractive choice for businesses looking for strong ML talent.

3. Brazil

With the largest AI and ML talent pool in Latin America, Brazil is a powerhouse for nearshore tech hiring. Its engineering education system produces highly skilled graduates, and the country’s ML sector has grown significantly. The University of Sao Paulo is ranked #1 in Latin America for machine learning and 85^th in the world. 

Brazil also has a strong community of AI researchers and data scientists, making it a leading destination for businesses seeking cutting-edge ML expertise.

4. Colombia

Colombia’s tech industry is rapidly expanding, particularly in Medellín, which has become a major AI and data science hub. The country has over 150,000 tech professionals, with 13,000+ ICT graduates entering the workforce each year. 

Colombian engineers are also known for their strong English proficiency, making communication with US-based teams easy.

5. Costa Rica

Costa Rica is actively investing in AI and machine learning development, with the government launching the National Artificial Intelligence Strategy (ENIA) 2024-2027 to promote ethical AI adoption. 

With growing government support and a well-educated workforce, Costa Rica is becoming a rising player in the LatAm ML industry.

At the end of the day, no matter which country you hire from, a trusted staffing partner or recruitment company will help you find the best nearshore ML engineers suited to your business needs.

Key Skills to Look for in a Nearshore Machine Learning Engineer

Finding the right nearshore machine learning engineer means looking at both technical expertise and finding someone who can thrive in a remote, collaborative environment while bringing strong AI and data science skills to the table.

When evaluating candidates, here are the essential ML technical skills to look for:

• Programming proficiency: Python is king in ML, and top engineers should be skilled in frameworks like TensorFlow, PyTorch, and Scikit-learn. Some roles may also require R or C++, depending on the project.
• Big data experience: Working with large-scale datasets is a must. Engineers should know Hadoop, Spark, and Amazon S3 to handle complex data pipelines.
• Cloud computing: ML models are often deployed on AWS SageMaker, GCP Vertex AI, and Google Cloud Storage, so cloud expertise is key.

A solid educational background in mathematics, physics, computer science, or engineering is also a strong indicator of an engineer’s ability, so be sure to ask for these during ML recruitment interviews.

Since hiring a nearshore ML engineer means they’ll be working remotely, these soft skills are just as important:

• Clear communication: Look for candidates who can explain complex ML concepts in simple terms and aren’t afraid to ask questions. In remote work, over-communication beats under-communication every time.
• Self-management: The best remote engineers don’t need someone looking over their shoulder. They set their own deadlines, provide updates without prompting, and flag issues before they become problems.
• Cultural alignment: This isn’t about making small talk about American sports. It’s about understanding US business expectations—meeting deadlines, being accountable, and bringing solutions along with problems.

They should also be able to adapt to your company’s specific culture, whether that’s a fast-paced startup environment, a methodical enterprise setting, or anything in between. Great remote hires pick up on team dynamics through video calls and chat and adjust their communication style accordingly without needing explicit instructions on “how things work here.”

• Collaboration skills: Remote ML engineers need to work effectively with your data scientists, product managers, and other stakeholders without the benefit of in-person interactions. This might mean effectively using Slack for day-to-day communication, project management tools like Jira or Asana for tracking progress, version control with Git, and collaboration platforms like Google Workspace or Microsoft Teams. 

In our experience, these soft skills often make the difference between a successful hire and one that doesn’t last. Technical skills get candidates in the door, but these traits determine whether they become valuable, long-term team members.

How to Find a Nearshore Machine Learning Engineer

Hiring a nearshore machine learning engineer can be done through several channels, but some are more efficient than others. 

Recruitment and staffing agencies: The most hassle-free option

Working with a specialized nearshore recruitment agency saves you from the guesswork and hidden complexities of international hiring. These partners offer critical advantages that most companies simply can’t replicate on their own:

• Local market expertise: They know which companies in LatAm are equivalent to working at a FAANG company in the US, which universities produce the strongest ML graduates, and where to find candidates with specific AI specializations.
• Comprehensive vetting: Beyond technical assessments, agencies conduct thorough reference checks in Spanish or Portuguese, verify employment history with local companies, and evaluate communication skills specifically for remote collaboration.
• Cultural compatibility assessment: They can assess whether candidates will mesh with your company culture and work style.
• Administrative support: The best partners handle contracts, compliance with local labor laws, international payments, and even help with equipment provisioning—eliminating headaches that often derail direct international hiring.

While this approach typically costs more than DIY hiring, the time saved and risk reduction more than justify the investment. You’ll get pre-vetted candidates who are ready to contribute from day one, rather than spending months navigating an unfamiliar talent landscape with uncertain results.

Freelance platforms: Ideal for short-term projects

Platforms like Upwork and Toptal offer access to freelancers specializing in ML engineering, but experience levels and quality can vary.

This approach works particularly well when you:

• Need specialized ML expertise for a specific project with a defined scope
• Want to test a working relationship before committing to a longer-term arrangement
• Have a fluctuating workload that doesn’t justify a full-time hire

The key is knowing how to evaluate talent effectively. The best freelance ML engineers typically have a solid portfolio of completed projects, specific expertise in frameworks like TensorFlow or PyTorch, and verifiable references from previous clients.

While freelancers may charge higher hourly rates than full-time hires, the flexibility to scale resources up or down as needed can ultimately save money. Just be prepared to invest time in clearly defining project requirements and milestones to ensure everyone’s on the same page from day one.

Job boards and referrals: The DIY direct hire

Going the direct route through job boards and professional networks can work well for companies willing to invest time in the hiring process. This approach gives you complete control and can be effective if you:

• Have team members who speak Spanish or Portuguese and understand the local tech landscapes
• Are willing to develop customized assessment processes for ML skills
• Already have some connections in the Latin American tech community

Posting on region-specific platforms like Get On Board, Remotar, or Computrabajo alongside global sites like LinkedIn and Indeed can attract qualified candidates without agency fees. Leveraging referrals from your existing network or team members can also yield high-quality candidates pre-vetted by people you trust.

The trade-off is clear: you’ll save on recruitment fees but spend significantly more time screening candidates and navigating the complexities of international hiring. For companies with previous experience hiring in Latin America or those looking to build their own recruitment capabilities, this approach offers valuable flexibility and direct candidate relationships.

At the end of the day, partnering with a recruitment firm that specializes in hiring from Latin America is by far the easiest way to find high-quality nearshore ML talent. Yes, we might be a little biased, but we believe it’s truly the best way to source talent when you aren’t familiar with a local talent market.

5 Nearshore Companies That Can Help You Hire an ML Engineer in LatAm

Unless you already have connections in Latin America or experience hiring there, you’ll likely want a partner to help navigate the talent landscape.

While you could tackle this on your own, most US companies find that working with a specialized nearshore hiring firm saves them time, reduces hiring risks, and gets them better candidates faster. Think of it as having local experts on your side who understand both the technical requirements and the regional nuances.

The companies listed below have been selected based on industry reputation, expertise in nearshore hiring or staff augmentation, and their ability to source skilled ML engineers. This isn’t a ranked list—each of these firms can help you find the right nearshore talent depending on your hiring needs.

1. Near

We’re up first. At Near, we don’t just help you find cost-effective ML talent—we connect you with engineers who align with your business culture and tech stack. Our team takes the time to understand your needs, meaning you get a perfect fit for your projects and with the rest of your team.

We focus exclusively on Latin America, which means we know the territory inside and out—from how to evaluate talent to what makes a competitive offer. Our recruitment team is based in the region, building personal relationships with top talent who have experience working with US companies. We’ve helped hundreds of US companies transform their growth trajectory through strategic hiring in Latin America.

Key features:

• Tailored ML hiring: We match you with engineers who fit your tech stack, industry needs, and company culture.
• Pre-vetted nearshore talent: Every ML engineer candidate undergoes technical assessments, language screening, and reference and international criminal background checks.
• Transparent, low-risk pricing: Interview candidates for free, then pay a monthly fee per hire once you’ve found your match. Replacement guarantee in case of compatibility issues. Most clients save 30–70% compared to equivalent US salaries while offering competitive compensation for LatAm professionals.

2. STX Net

STX Next provides end-to-end machine learning development services. With a strong presence in Mexico and other nearshore locations, it specializes in staff augmentation and dedicated ML teams for US companies. 

Its expertise spans big data processing, AI consulting, and deep learning solutions, making it a trusted choice for companies looking to scale their AI capabilities.

Key features:

• Comprehensive AI solutions: Offers predictive maintenance, computer vision, and MLOps for business workflow optimization with AI-powered insights.
• Flexible engagement models: Provides nearshore staff augmentation for companies needing dedicated ML engineers or full-cycle project development.
• Proven industry experience: Works across multiple sectors, delivering tailored AI and ML solutions that align with business objectives.

3. Peterson Technology Partners

Peterson Technology Partners (PTP) is a technology staffing firm specializing in nearshore AI and machine learning talent. With 25 years of recruiting experience, it sources ML engineers from Argentina, Mexico, and Brazil, helping US companies build remote and hybrid teams. 

PTP provides contract, direct-hire, and nearshore staffing solutions, streamlining the hiring process with AI-powered recruitment tools and an extensive global talent pool.

Key features:

• Nearshore AI/ML hiring expertise: Offers pre-vetted ML engineers from Latin America for cost-effective, time zone-aligned hiring.
• Advanced tech stack: Engineers specialize in Python, R, C++, Java, and Lisp, with expertise in Big Data, NLP, and deep learning models.
• Custom job optimization: Assists companies in creating job descriptions to attract the correct talent for the job.

4. Bertoni Solutions

Bertoni Solutions specializes in staffing and staff augmentation for AI and machine learning roles, primarily sourcing talent from Brazil and Peru. 

With a rigorous selection process and a 2000+ strong technical talent pool, it connects businesses with ML engineers who can seamlessly integrate into existing teams. 

Bertoni Solutions focuses on rapid deployment, scalability, and cost efficiency.

Key features:

• Strong LatAm presence: Staffing centers in Peru and access to LatAm talent from Argentina, Brazil, and Colombia.
• Advanced AI/ML tech stack: Engineers are proficient in Python, TensorFlow, Keras, and PyTorch, covering a wide range of AI development needs.
• Rapid hiring and deployment: Provides a customized shortlist of candidates within 24 hours for urgent AI/ML projects.

5. Jobsity

Jobsity is a nearshore staffing firm that helps businesses hire machine learning engineers across Latin America, with a strong presence in Brazil, Argentina, and Peru. 

Its hiring process includes technical screening, coding challenges, and client evaluations, meaning that candidates have both strong ML skills and cultural alignment.

Key features:

• Thorough vetting process: Candidates go through multiple screening stages to ensure the right talent is matched with the right company.
• Long-term team integration: Most hires work with the same client for three years or more, making it ideal for businesses needing consistent, stable AI/ML talent.
• Straightforward hiring and support: Provides flat-rate pricing, a dedicated Client Success Manager, and support with HR and payroll administration.

Final Thoughts

Nearshore hiring puts top machine learning talent within reach for companies of all sizes. Latin American engineers bring the technical expertise needed to build sophisticated AI systems along with the collaboration skills that make remote work successful. Beyond the immediate cost savings, you’re gaining access to professionals who are motivated, culturally aligned with US business practices, and working in compatible time zones.

The best nearshore ML professionals don’t just execute tasks—they become integral team members who contribute ideas, solve problems creatively, and help drive your AI initiatives forward. With the right hiring approach, you’ll build a team that delivers exceptional value while giving you the flexibility to scale your ML capabilities as your business grows.

At Near, we’ve helped hundreds of US companies build high-performing teams with LatAm talent, including specialized roles like machine learning engineers. We handle the entire process—from sourcing and vetting candidates to managing payroll and compliance—so you can focus on growing your business rather than navigating international hiring complexities.

Book a free consultation today, and let’s find the right ML talent for your business.