Get Computer Vision Engineers
Get Computer Vision Engineers from Latin America
Vision ML engineers from Brazil, Argentina, and Colombia who build object detection, image classification, segmentation, and video analysis systems. They take models from training through to production inference, not just research notebooks.
Why LATAM for computer vision engineering
Cost advantage on specialized talent
Senior CV engineers in the US earn $185k-$240k. LATAM equivalents deliver at $82k-$125k, meaningful savings for roles that require both ML depth and systems engineering.
Timezone overlap
LATAM's 4-8 hour overlap with US teams supports collaborative annotation review and model evaluation sessions that benefit from synchronous feedback.
Research and production communication
CV engineers bridge research papers and production systems. LATAM engineers in this space are used to communicating model accuracy vs inference latency tradeoffs to product teams.
Strong academic CV research
LATAM universities produce computer vision researchers. Several engineers in our network have graduate CV backgrounds with publications or thesis work in object detection or medical imaging.
Edge deployment experience
Vision applications often run on edge hardware. LATAM CV engineers increasingly have experience deploying optimized models to Jetson devices, Raspberry Pi, and mobile (Core ML, TFLite).
What our computer vision engineers know
PyTorch
TensorFlow / Keras
ONNX export
Docker + Triton Inference ServerHow we vet computer vision 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, model selection: given a detection task (objects, defects, or faces in a specific environment), candidates justify their model choice based on accuracy requirements, inference latency targets, and hardware constraints. Second, training setup: data augmentation strategy with Albumentations, handling class imbalance, mAP evaluation, and hyperparameter tuning approach. Third, production deployment: how they'd export a model to ONNX, run it through TensorRT, deploy to a Triton Inference Server, and set up a monitoring pipeline to catch performance degradation on a new input distribution.
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 CV Engineer
São Paulo, Brazil
4+ years experience
Senior CV Engineer
Buenos Aires, Argentina
7+ years experience
Lead CV Engineer
Florianópolis, Brazil
10+ years experience
Want to see profiles matched to your specific requirements?
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What to expect
What sets our computer vision engineers apart
CV engineers in our network build systems that work in the real world, not just on benchmark datasets. They handle lighting variation, occlusion, domain shift, and latency requirements. They use AI tools to accelerate their own development workflow, particularly for annotation and architecture exploration.
Foundation models (SAM, Grounding DINO) are used to accelerate annotation workflows: auto-labeling reduces manual annotation time significantly on new datasets.
AI-assisted architecture search: they use tools like Copilot alongside papers-with-code to explore model variants faster before committing to a training run.
They define evaluation beyond mAP: they look at false positive rates by class, failure modes under specific lighting or camera conditions, and inference latency at the 99th percentile.
Model cards document training data distribution, failure modes, and intended deployment conditions. Future engineers don't discover edge cases in production.
They communicate production vs research performance gaps honestly. If a model performs 95% mAP on the test set but 78% on live video, they say so before deployment.
LATAM vs US computer vision engineer salaries
Junior CV Engineer
Mid CV Engineer
Senior CV Engineer
Lead CV Engineer
FAQ
Common questions
Ready to meet your computer vision engineers?
Tell us your vision use case and deployment target. We'll have a shortlist in 3 days.