How to Hire a Nearshore Machine Learning Engineer in 2026: Top Benefits, Countries, and Companies

Hiring a skilled machine learning engineer in the US has become one of the hardest and most expensive searches in tech, and demand keeps outpacing supply. That's why more US companies are hiring nearshore machine learning engineers in Latin America: the same technical depth, at 30–70% lower cost, working your hours.

This guide covers what you need to hire a nearshore machine learning engineer in 2026: the benefits, the best countries in Latin America, the skills to look for, and the 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. The US Bureau of Labor Statistics projects employment of data scientists, the closest occupation it tracks to ML engineering, to grow 35% from 2025 to 2035, much faster than the average for all occupations. 

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 for US businesses, where you can save 30–70% on salary costs while working with engineers who share most of your working hours.

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 day-to-day collaboration easier.

You’re also tapping into talent pools in countries where technology education and innovation are thriving.

Here's what machine learning engineers earn in Latin America compared with the US, according to Hire With Near's salary benchmarks:

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For the most up-to-date figures, see Hire With Near's US vs Latin America Salary Guide.

If you're hiring for other data roles too, our Data Roles Salary Guide covers data scientists, data engineers, and data analysts.

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Where to Find Nearshore Machine Learning Engineers

While ML talent exists throughout Latin America, five countries stand out for depth, infrastructure, and specialization: Mexico, Argentina, Brazil, Colombia, and Costa Rica.

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.

Mexico’s AI sector grew from 34 companies in 2018 to 362 in 2024, according to Endeavor and Santander México. Those companies attracted around $640 million in investment in 2022 and 2023, and machine learning makes up 23% of the sector. 

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.

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

Related reading: Hiring in Argentina: Great Talent, Significant Savings, and What US Companies Need to Know 

3. Brazil

With the largest developer community in Latin America, which GitHub projects will reach 19.6 million developers by 2030 (third-largest in the world), 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 São Paulo (USP) ranks #1 in Latin America and 88th in the world for machine learning research, according to EduRank.

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

4. Colombia

Colombia’s tech industry is rapidly expanding, particularly in Medellín, where the World Economic Forum is launching a Centre for the Fourth Industrial Revolution to serve as an AI innovation hub. 

Medellín and the surrounding Valle de Aburrá employed nearly 108,000 people in the technology sector as of July 2025, according to ACI Medellín, and Antioquia’s universities produced more than 3,900 IT graduates in 2023.

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.

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.

For the wider picture, including related roles, see our guide to how to hire data and AI talent in Latin America.

What Are the 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, often alongside the data engineers who build them.
• 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. 

Experience matters as much as credentials: in Hire With Near's 2026 State of LatAm Hiring Report, 98% of engineering placements went to mid-level or senior professionals.

When writing your ML engineer job description, clearly specify which technical skills are must-haves versus nice-to-haves to attract candidates with the right experience level.

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: Strong remote hires understand 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 teammates 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.

Hire With Near's recruiters also treat communication as a technical signal: an ML engineer who can't explain a project they built and deployed to someone who isn't an engineer may be repeating terminology without fully understanding it. Technical skills get candidates in the door, but these traits determine whether they become valuable, long-term team members. 

For more on vetting, see the questions hiring managers ask about hiring data and AI talent in Latin America.

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How Can You 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. That removes the paperwork that often derails 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 make sure 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. Asking your existing network or team members for referrals 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 handling contracts, payroll, and local compliance yourself. Our free guide to hiring remotely in Latin America walks through what that involves. 

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.

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. Hire With Near

Hire With Near is a full-service staffing and recruiting agency that helps US companies of all sizes hire top-performing remote talent in Latin America across finance, accounting, sales, software engineering, AI, data, design, marketing, operations, and virtual assistance. It ranked No. 118 on the 2026 Inc. 5000 list of the fastest-growing private companies in America.

We specialize in AI and ML recruitment, connecting US businesses with machine learning engineers who bring both technical depth and the collaboration skills needed for remote AI development.

Our done-for-you hiring process typically delivers a shortlist of candidates in 3 to 5 days, and most of our clients make a hire in under three weeks. Our LatAm ML engineers work with overlapping working hours, so your team can review models and fix pipeline issues together in real time.

With Hire With Near, you pay nothing upfront. You choose between a one-time placement fee or a monthly fee, including payroll and compliance, after making a hire.

We fill 97% of the roles we take on, hold a client satisfaction score of 9.1, and have a pre-vetted talent pool of 160,000+ candidates, but we don't just search our database. 

We find ML engineers who match your exact technical requirements, from specific Python frameworks to industry experience, so they fit the way your team already works.

Hire With Near isn't the right fit for every ML project, though. If you want a partner to build and deliver an ML product end to end, a development company like STX Next is the better call. If you need a contractor on a short, urgent project, a firm that offers contract staffing, like Peterson Technology Partners, makes more sense.

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 fee per hire once you’ve found your match. Every placement is backed by a 180-day replacement guarantee. Most clients save 30–70% compared to equivalent US salaries while offering competitive compensation for LatAm professionals.
• Reputation: 4.9 rating on G2 across 160+ reviews.

2. STX Next

STX Next provides end-to-end machine learning development services. Headquartered in Poland, it opened a nearshore delivery center in Mérida, Mexico, in 2022, and 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. Founded in 1997, it brings nearly 30 years of recruiting experience and helps US companies build remote and hybrid teams.

PTP provides contract, direct-hire, and nearshore staffing solutions, simplifying 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 talent pool of 3,000+ vetted professionals, it connects businesses with ML engineers who can 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.
• Hiring timeline: Most hires are completed within 1 to 8 weeks.

5. Jobsity

Jobsity is a nearshore staffing firm that helps businesses hire machine learning engineers across Latin America, with talent in 20+ Latin American countries.

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: Its average staffer works 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 US 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 gain access to professionals who are motivated, culturally aligned with US business practices, and working in compatible time zones.

The best nearshore machine learning engineers become integral team members who contribute ideas, solve problems creatively, and help drive your AI initiatives forward, often working with the AI developers who turn their models into products. With the right hiring approach, you’ll build a team that delivers exceptional value and gives you the flexibility to scale your ML capabilities as your business grows.

ParkMobile shows what that looks like in practice. The parking and mobility platform had run its engineering work through third-party agency developers for years and wanted its own senior team instead, in Colombia, a market it had never recruited in before. 

Hire With Near placed 9 senior engineers across platform, backend, and iOS roles at a 24-day average time to hire, and ParkMobile now saves $1,066,085 a year against US rates, a 73% reduction. 

Hear Akshay Gokarnakar, ParkMobile's TA Lead, describe the collaboration in the video below:

At Hire With Near, we’ve helped 950+ 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 instead of on contracts, payroll, and local labor rules.

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

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