Top Machine Learning Jobs in Brazil

Design and build scalable full-stack AI-driven applications and microservices on AWS/GCP. Integrate LLMs, vector databases, agentic workflows, and AI services to automate workflows, create APIs, UIs, and AI agents. Contribute across the SDLC, including deployment, monitoring, and testing.

Identify Brazilian species through images and audio, support biodiversity surveys, and train machine learning models by annotating data.

The Senior Machine Learning Engineer will enhance Xometry's ML capabilities, develop models for cost prediction, lead technology evaluations, and collaborate on AI solutions.

The Fraud Strategy Specialist will develop and manage fraud prevention strategies, enhance analytics, collaborate with teams, and ensure a seamless customer experience.

The Fraud Strategy Specialist will design fraud prevention strategies, develop anomaly detection systems, and collaborate with multiple teams to enhance safety while enabling customer growth.

Perform part-time remote data tasks to improve English AI outputs: collect, evaluate, and annotate data; conduct pairwise comparisons and counting tasks; tag and label objects across audio, video, images. Flexible, long-term freelance work based in Brazil.

The Senior Data Scientist will design and build scalable data products, develop machine learning models, and collaborate with various teams to drive impactful data solutions.

Join the Data Marketing Consulting team to execute data-driven marketing projects: deliver analytics solutions, collaborate with Activation/Creativity/Strategy, build client relationships, communicate results, research technical approaches, and contribute best practices to ensure business value for clients.

Design, build, test, and maintain RPA automations (UiPath/Blue Prism), integrate RPA with enterprise systems, document solutions, support UAT/rollout, and troubleshoot production bots within agile teams.

The Customer Solutions Manager engages with customers, leads adoption and success, collaborates with teams, and translates technical demos into business value, while maintaining customer relationships and providing insights to improve offerings.

The VP of Product Development will lead a global engineering team for eDiscovery platforms, focusing on modernization, reliability, and AI integration while managing execution and stakeholder communication.

Design and deploy a multi-agent orchestration layer for conversational AI, build production-grade Python ML services, lead end-to-end ML projects, establish team best practices, mentor engineers, and ensure secure, scalable, high-performance customer-facing AI systems.

As a Data Engineer, design and maintain data pipelines and infrastructure, collaborate with teams for efficient data delivery, and optimize data storage and retrieval processes.

Partner with LATAM sales to lead technical discovery, deliver demos and POCs, design solution architectures, resolve technical blockers, and support integrations across Telnyx products (voice, messaging, wireless, networking, storage, and AI). Act as technical expert through the sales cycle and convey technical value to both technical and executive audiences.

As an Analytics Engineer, you'll refine data, build models, enable self-service analytics, and collaborate with various teams to improve customer insights.

Lead end-to-end supply chain transformation engagements, advising executives and directing multidisciplinary teams to improve planning, procurement, logistics, inventory, and supplier performance. Design future-state architectures, build business cases, drive implementation, grow client relationships, and scale practice capabilities while delivering measurable operational and financial impact.

Lead end-to-end supply chain transformation engagements for commercial and utility clients. Advise executives, design future-state supply chain architectures, oversee implementation across planning, procurement, logistics, and analytics, build financial value cases, and manage multidisciplinary teams to deliver measurable operational and financial results.

Design, build, and deploy ML propensity and recommendation models on Databricks. Develop data pipelines for training and scoring, run A/B tests for incentives/pricing, analyze results, and translate insights into business actions. Collaborate with channel teams and stakeholders, rapidly prototype solutions, and document methods for reproducibility.

Work with client-provided data to clean, validate, preprocess, and engineer features. Train and tune machine-learning models (avoid overfitting), optimize runtime, and communicate results to stakeholders. Collaborate with client teams and internal Product/Research to deploy and improve risk-detection solutions.

The Data Product Manager will lead AI-powered computer vision initiatives, collaborating with global teams, managing product planning, and ensuring successful delivery and adoption across markets.

Lead LATAM transaction monitoring end-to-end: manage team performance, tune pre/post-transaction rules, partner on ML detection, oversee investigations and SAR/STR filing, coordinate regulators/audits, drive automation roadmap, and own KPIs and QA.

Responsible for leading digital transformation through scalable and resilient architectures, integrating AI, ensuring security, and developing data-driven solutions for the organization.

Mine and analyze internal and external datasets to create data-driven press hooks, visualizations, and social content. Collaborate with PR, data engineering, and growth teams to produce viral social posts, reports, and press materials using automation and AI tools to scale workflows.

Perform preventive monitoring and investigations of fraud and partner misconduct for the Phone Finance product. Analyze risk indicators, resolve fraud cases, develop prevention strategies, collaborate cross-functionally, run periodic risk assessments, produce reports, and support team training to mitigate portfolio risk.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.