Top Machine Learning Jobs in Brazil

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Deploy and maintain Shift's fraud detection solutions for a pod of clients: manage ETL and data pipelines, develop/test/tune/deploy models, roll out platform features, optimize production environments, engage stakeholders, report performance, and mentor junior data scientists.

This role involves leading strategic planning, product innovation, and cross-functional collaboration to enhance pharmacy analytics and drive growth initiatives for CVS Health.

Plan, build, and launch multi-channel demand-generation and lifecycle marketing campaigns for AI/GPU products. Own campaign execution, analytics, technical copywriting, global collaboration, event support, and operational support for senior marketing leaders to drive customer acquisition and retention.

Provide L2 application support: troubleshoot and resolve production issues, analyze logs and root causes, manage incidents and change processes, automate monitoring/health restoration, build runbooks, and produce health and trend reports to improve application stability.

Create and review realistic, locale-specific robotics and natural sciences tasks in Brazilian Portuguese; apply scoring rubrics; evaluate and score AI and human responses for accuracy and regulatory alignment; produce gold-standard solutions and expert feedback.

As the Head of Credit for Brazil, you will develop credit policies, lead analytics teams, build risk models, and collaborate across teams to drive growth and ensure quality in credit offerings.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Lead end-to-end delivery of Google Cloud adoption engagements. Coordinate cross-functional teams and executive stakeholders, scope and plan projects, mitigate risks, track adoption and utilization, integrate change management, mentor teams, and drive successful enterprise cloud migrations and modernization.

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

Design, develop, and deploy machine learning pipelines. Implement MLOps practices and optimize models. Collaborate on data pipelines and cloud deployment.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

The Senior Machine Learning Engineer will build and optimize ML systems for CRM marketing, collaborating with cross-functional teams to impact customer engagement and product functionality.

Develop and deploy data and AI solutions for financial risk and customer experience. Own the full machine learning lifecycle, including problem definition, model design, training, productionization, monitoring, and impact measurement. Collaborate with Operations, Engineering, Product, Fraud, Compliance, and Credit teams using statistical methods and strong software engineering practices.

Design, build, evaluate, and deploy production-grade LLM and classical ML solutions. Create retrieval and agentic workflows, integrate AI into client apps, implement evaluation frameworks, optimize prompts and pipelines, and collaborate with product teams through discovery to production support.

Lead design and development of AI solutions using Python and AWS, abstract reusable libraries, collaborate with data teams, participate in technical/customer discussions, follow Agile practices, and lead a team of developers to build AI systems.

Own the architecture, development, automation, and production deployment of end-to-end machine learning solutions. Manage the ML lifecycle, evaluate cloud architectures for performance and cost efficiency, integrate with data engineering and software teams, troubleshoot complex systems, align solutions with business needs, and mentor teams on MLOps adoption.

Design and evaluate memory architectures for foundation models across modalities. Define memory performance metrics, build production-ready scalable learning models, collaborate with researchers and engineers, and recommend roadmap improvements based on model assessments.

Design, build, and deploy ML models and end-to-end pipelines; collaborate with data and MLOps teams to integrate, monitor, retrain, and optimize models; implement cloud-based scalable solutions and document processes.

Build, deploy, and maintain production-grade ML models and data pipelines in cloud environments. Perform EDA and feature engineering, monitor model performance and drift, and collaborate with engineering and business stakeholders. Contribute to AI/LLM initiatives and ensure solutions operate reliably in production.

Lead design and delivery of end-to-end ML pipelines and platform architecture for BEES AI. Standardize model development, ensure reproducibility, implement observability for training and production, and optimize scalable, cost-effective deployments while collaborating with data, platform, and product teams.

Implement and extend ML platform capabilities for training and inference; develop model development workflows; build observability for models; optimize deployments for cost and SLA; troubleshoot pipelines and share solutions.

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

As an AI/ML Engineer, design and implement AI solutions, collaborate with teams, build ML pipelines, and ensure quality and compliance in healthcare applications.