Top Machine Learning Jobs in Brazil

The Machine Learning Engineer collaborates to design, deploy, and maintain ML models for medical imaging, ensuring production readiness and optimization.

As a Senior Data Scientist at TRACTIAN, you will develop predictive models and algorithms to enhance equipment reliability and optimize production efficiency.

Responsible for full-cycle recruiting for senior AI roles with a focus on sourcing talent in LATAM and Europe, managing pipelines, and closing candidates.

As a Machine Learning Engineer/Data Scientist, you'll build and deploy ML solutions, collaborate with stakeholders, and analyze data, focusing on achieving measurable business impact.

Architect, build, and deploy low-latency, high-throughput ML systems across the full ML lifecycle: scalable feature pipelines, training/tuning of tree and deep models, production packaging/serving, MLOps automation, monitoring for drift, and rigorous A/B experimentation to drive business metrics.

The Senior Machine Learning Engineer at AB InBev will design and implement ML systems and pipelines to drive AI's mission for the Growth Group.

As a Senior Machine Learning Engineer, you will develop high-performance services, innovate AI/ML applications, design algorithms, and collaborate closely with teams to optimize ad systems.

The Senior Machine Learning Engineer will develop high-performance services, innovate ad technologies, and collaborate with teams to optimize ad campaigns through AI and ML solutions.

As a Senior MLOps & AI Infrastructure Engineer, you'll build and maintain ML infrastructures, focusing on Feature Store, automation, and data pipelines, ensuring compliance and collaboration with data science teams.

As a Mid-Level Machine Learning Engineer, you will deploy and maintain ML models, design APIs, and improve model performance, handling real-time data integration.

The Data Scientist will lead data-driven decision-making, develop machine learning models, design automated pipelines, and improve product performance.

Design, deploy, monitor, and maintain ML models and ML-enabled systems across batch and real-time contexts. Build pipelines, features, tooling and platforms, collaborate with teams, and ensure operational and business performance of ML solutions.