Get Machine Learning Engineers
Get Machine Learning Engineers from Latin America
ML engineers from Brazil, Argentina, and Colombia who take models from research to production. They build training pipelines, deploy to serving infrastructure, monitor for drift, and optimize latency, not just notebooks.
Why LATAM for machine learning engineering
Strong cost advantage
ML engineers in the US earn $175k-$230k. LATAM equivalents deliver at $78k-$120k, particularly relevant for production ML roles that require ongoing model management.
Timezone overlap
LATAM's 4-8 hour overlap with US teams is practical for ML work that involves daily coordination with data scientists, product managers, and backend engineers.
Research-to-production communication
ML engineers bridge research and software engineering. LATAM engineers in this space are used to translating model performance metrics into product impact for non-technical stakeholders.
Strong academic ML background
LATAM universities (USP, UBA, UFRJ, Universidad de Chile) produce ML researchers with strong mathematical foundations. Several engineers in our network have graduate-level ML training.
MLOps maturity
LATAM ML engineers increasingly work in production MLOps environments (feature stores, model registries, A/B testing infrastructure, and drift monitoring), not just research settings.
What our ML engineers know
PyTorch
TensorFlow / Keras
scikit-learn
Pandas / NumPy / Polars
Vertex AI / SageMaker / Azure ML
Docker + Kubernetes for ML
FastAPI / TorchServe (model serving)
Apache Spark for ML at scaleHow we vet ML engineers
Every candidate completes all seven stages before you see their profile. You can also run your own technical round after our screening.
* For common roles like full-stack. Other roles typically take 7 to 10 days.
CV check
Stage 1We review experience, stack, seniority, and career history against the role before anyone is invited to interview. Most applicants stop here.
Behavioral interview
Stage 2A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
Technical interview 1
Role-specificA 90-minute live session covering three areas. First, training pipeline design: candidates walk through how they'd build a reproducible training pipeline for a classification problem, including data splits, feature engineering, experiment tracking, and model selection. Second, production readiness: how they'd serve a model via FastAPI, handle batch vs real-time inference, and set up latency monitoring. Third, debugging a real scenario: a deployed model's precision has dropped 15% over 30 days: they walk through root cause analysis (data drift, label drift, distribution shift) and the remediation steps.
Technical interview 2
Stage 4A second, deeper round on system design, architecture tradeoffs, and code review, matched to the seniority you are hiring for.
Take-home assessment
Stage 5A practical assignment that mirrors real work. We review code quality, testing, documentation, and how the candidate explains their decisions.
Reference check
Stage 6We speak with at least two professional references who worked with the candidate, not personal contacts.
Background check
Stage 7Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Who you might hire
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
Mid ML Engineer
São Paulo, Brazil
4+ years experience
Senior ML Engineer
Buenos Aires, Argentina
7+ years experience
Lead ML Engineer
Medellín, Colombia
10+ years experience
Want to see profiles matched to your specific requirements?
Request matched profiles
What to expect
What sets our ML engineers apart
ML engineers in our network know the difference between a model that performs well in a notebook and one that works in production. They track experiments, version data, monitor deployed models, and rebuild pipelines when distributions shift. They also use AI tools, both as practitioners who deploy them and as engineers who use them in their own work.
GitHub Copilot and Cursor for data processing scripts, model training boilerplate, and FastAPI serving code. They use coding assistants to move faster on the implementation side of ML work.
AutoML tools (Vertex AI AutoML, Auto-sklearn) are used for model selection exploration, not as a replacement for engineering judgment. They know when AutoML results are trustworthy.
They evaluate foundation models for fine-tuning and RAG applications and communicate clearly about the infrastructure cost and latency implications before committing to a model choice.
Experiment tracking is non-negotiable. Every training run has logged parameters, metrics, and artifacts. Reproducibility is a design requirement, not an afterthought.
They own production model performance: they set up monitoring dashboards, define drift thresholds, and trigger retraining pipelines before users file bug reports.
LATAM vs US ML engineer salaries
Junior ML Engineer
Mid ML Engineer
Senior ML Engineer
Lead ML Engineer
FAQ
Common questions
Ready to meet your ML engineers?
Tell us your ML use case and infrastructure. We'll have a shortlist in 3 days.