A machine learning engineer builds the systems that take machine learning models from research to production - deploying, scaling, and monitoring ML models in real business applications.
A great Machine Learning Engineer does not just complete tasks. They own a function that directly frees you to grow. Here is what that looks like in a scaling business:
01Designing, building, and deploying ML training pipelines and model deployment infrastructure
02Constructing and maintaining data pipelines to automate data collection, preprocessing, and feature engineering.
03Optimize model performance, latency, and cost in production
04Collaborate with data scientists on model architecture and feature engineering
05Build and maintain ML platform tooling for the team
How this hire moves your business forward: Most ML models never make it to production. A machine learning engineer is what closes that gap - turning data science research into reliable, scalable products that generate real business value.
Why LatAm
Why LatAm Produces Great Machine Learning Engineers.
LatAm machine learning engineers are increasingly increasingly proficient in creating end-to-end AI solutions., with many coming from strong computer science and mathematics programs combined with production ML experience at tech companies.Regional Hubs: Mexico, Brazil, and Argentina
The timezone overlap with the US is strong. LatAm professionals work within 1 to 3 hours of US Eastern time, so there is no async lag, no late-night handoffs, and no communication gap.
Skills & tools
Know What a Great Machine Learning Engineer Actually Brings to the Table.
Beyond the resume, here are the skills, tools, and traits that separate strong performers from strong interviewers.
Hard Skills
ML model deployment (MLflow / Kubeflow / SageMaker)
Python (PyTorch / TensorFlow / scikit-learn) for building, training, and deploying machine learning models.
ML infrastructure and pipelines
Feature stores and data pipelines for ML
Model monitoring
Common Tools
Python / PyTorch / TensorFlow
MLflow / Weights & Biases
AWS SageMaker / Vertex AI
Kubernetes / Docker
Airflow
Soft Skills & Traits
Engineering rigor applied to ML systems
Collaborative with data scientists
Production reliability-focused
Curious about both ML and systems
Performance optimization mindset
Compensation
What You Can Expect to Pay.
Based on Sur market data and regional benchmarks. Figures reflect total cash compensation.
Seniority
US Annual
LatAm Annual
You Save
Entry
$100,000
$40,800
$59,200 / yr
Mid level
$140,000
$55,200
$84,800 / yr
Senior
$185,000
$74,400
$110,600 / yr
Spot the right hire
What to Look For, and What to Watch Out For.
Green Flags
Has deployed ML models to production that are actively serving predictions
Can explain their approach to model monitoring and drift detection
Bridges data science and engineering effectively
Production systems are reliable and performant, not just accurate
Red Flags
Has only trained models in notebooks without production deployment experience
Cannot explain the difference between model training and model serving
No experience with model monitoring or drift detection
Our process
Our Process for This Role.
We do not post and wait. Every Machine Learning Engineer search we run is built from scratch around your business, your stage, your team, and your goals. And at every step, we are thinking about how this hire helps you grow.
1
Onboarding Call
We start by understanding what you actually need.
2
Role Scoping and Assessment Design
We build a precise role profile and design the custom skills assessment before we search for anyone.
3
Sourcing
We source actively across LatAm and the Caribbean and through our network.
4
Prescreening and Phone Screen
Every candidate is internally screened then put through an English phone screen.
5
Your Shortlist
3 to 5 candidates delivered early in the process with background, audio clip, and our team's recommendation.
6
Skills Assessment
Shortlisted candidates take a custom assessment built to replicate the actual work of the role.
7
Hire and Guarantee
We support the offer, help structure compensation for retention, and back every placement with a 90-day guarantee.
Common Questions About Hiring a Machine Learning Engineer.
3-4 weeks typically. Most placements are made within 21 days of the onboarding call.
Most LatAm professionals work within 1 to 3 hours of US Eastern time.
All Sur placements speak fluent English. We screen for language ability on every search. Moderate English acceptable depending on exposure.
A LatAm ML engineer delivers the same production ML capability as a US hire at 35-45% of the cost. Strong CS and ML backgrounds are available, particularly in Argentina, Brazil, and Colombia.
If your hire does not work out within the first 90 days for any reason, we replace them at no additional cost.
Once you have models worth deploying. Data scientists build models. ML engineers scale and maintain them in production.
AWS SageMaker has the largest ecosystem. Vertex AI on GCP is strong for teams already on Google Cloud. Azure ML is right for Microsoft-heavy environments.
Ready to Hire a Machine Learning Engineer Who Actually Moves the Needle?
Let us design the role together and find you the right person from LatAm.