Get Machine Learning Engineers
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.
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.
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.
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.
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.
LATAM ML engineers increasingly work in production MLOps environments — feature stores, model registries, A/B testing infrastructure, and drift monitoring — not just research settings.
Every candidate completes all five stages before you see their profile. You can also run your own technical round after our screening.
A timed test measuring analytical thinking, pattern recognition, and problem-solving clarity, independent of specific programming language knowledge.
A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
A 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.
Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
We speak with at least two professional references who worked with the candidate in an engineering context, not personal contacts.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
São Paulo, Brazil
4+ years experience
Buenos Aires, Argentina
7+ years experience
Medellín, Colombia
10+ years experience
Want to see profiles matched to your specific requirements?
Request matched profilesWhat to expect
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.
Junior ML Engineer
Mid ML Engineer
Senior ML Engineer
Lead ML Engineer
FAQ
Tell us your ML use case and infrastructure. We'll have a shortlist in 3 days.
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 scale