Top Machine Learning Jobs in Brazil

Lead the design, development, deployment, and continuous improvement of scalable production AI solutions. Build LLM, RAG, agentic AI, machine learning, and document intelligence systems using Python, Azure, DevOps, IaC, Docker, Kubernetes, and MLOps. Own architecture, testing, evaluation, monitoring, security, governance, and operational support while translating business needs into measurable outcomes. Provide technical leadership, mentorship, stakeholder collaboration, and influence enterprise AI engineering standards and platforms.

Lead and grow a backend engineering team responsible for DoorDash’s Credits and Refunds, resolution workflows, and policy configuration platform. Own platform migration, distributed-systems architecture, reliability, scaling, configuration safeguards, and integration of machine-learning models into high-volume production workflows. Partner with Product, ML, Fraud, Data Science, and Operations teams while mentoring engineers, shaping technical direction, improving operational excellence, and supporting engineering recruiting.

Build AI systems for legal teams by designing legal datasets, annotating training and benchmarking data, defining model requirements, translating legal workflows into structured processes, validating product capabilities, engaging clients, and prototyping with AI tools and scripting. Requires a JD and active or prior bar membership, 2–8+ years of legal practice, strong familiarity with litigation, investigations, compliance, or e-discovery workflows, and comfort collaborating with engineers and product teams.

Design, develop, evaluate, and deploy scalable AI and machine learning solutions for fraud prevention, chargeback risk, and payment risk. Build production-grade APIs, data pipelines, and services; manage CI/CD, monitoring, testing, and troubleshooting for ML systems. Collaborate with data science, engineering, MLOps, and business teams to deliver impactful solutions, while mentoring colleagues and improving AI development practices.

Architect, develop, and deploy machine learning systems for chatbot and search applications. Design experiments, analyze structured and unstructured data, improve models, and develop LLM-powered product features using prompt experimentation, agent evaluation, and AI guardrails. Provide technical leadership to data scientists and contribute to Data Science chapter decisions, reviews, and knowledge sharing.

Architect, develop, and deploy machine learning systems for chatbots, search, and other product features. Design experiments, analyze structured and unstructured data, improve models, and develop LLM-powered applications using prompt evaluation, agents, guardrails, and proofs of concept. Provide technical leadership within the Data Science chapter and contribute to technical decisions, reviews, and knowledge sharing.

Architects, develops, and deploys machine learning systems for chatbots and search. Designs experiments, analyzes structured and unstructured data, improves models, and applies scientific methods to LLM-powered applications. The role evaluates prompts, agents, capabilities, and guardrails while building generative AI proofs of concept. It also provides technical leadership and contributes to Data Science chapter decisions, reviews, and knowledge sharing.

Architects, develops, and deploys machine learning systems for chatbot and search applications. Designs experiments, analyzes structured and unstructured data, and develops LLM-powered applications using prompt experimentation, agent evaluation, guardrails, and generative AI proof-of-concepts. Provides technical leadership within the Data Science chapter and collaborates with product, design, engineering, and machine learning teams.

Build and deploy production-grade agentic AI and machine learning systems, including scalable backend services, data pipelines, RAG applications, and cloud infrastructure. Collaborate with ML engineers, data scientists, and clients to discover use cases, develop AI-powered products, optimize platform performance, and support deployments. The role also involves MLOps, model monitoring and versioning, automated testing, infrastructure management, research and development, and direct client engagement.

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.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Own a 0-to-1 product surface end-to-end, including vision, roadmap, specifications, shipping, metrics, and customer outcomes. Partner directly with customers on discovery, onboarding, growth reviews, and deployments. Collaborate with software, ML/AI, and data engineers on data pipelines, models, agents, and activation platforms. Prioritize customer requests, define measurable success criteria, and guide product decisions across teams and time zones.

Lead post-sale technical and commercial engagement for strategic Salesforce Data Cloud and Agentforce customers. Develop consumption, adoption, enablement, and mutual success plans; guide architecture, governance, integrations, and change management; identify risks; and accelerate platform usage, customer value, and account growth. Build executive relationships, facilitate discovery and workshops, coordinate internal sales, engineering, product, services, and support teams, and communicate product updates. Serve as a trusted technical advisor across CRM, data architecture, analytics, AI, and machine learning.

Build and maintain Brex’s accounting platform, including reusable records, rules, mappings, APIs, and ERP integrations. Develop customer-facing accounting workflows in React and TypeScript, design reliable distributed systems, and integrate ML-powered accounting automation with explainability and human review. Own solutions end to end across APIs, data models, scalability, observability, and user experience. Collaborate cross-functionally, participate in on-call rotation, and uphold strong engineering standards.

Designs, builds, and deploys production AI solutions using machine learning, deep learning, generative AI, and LLMs. Responsibilities include model development and evaluation, RAG pipelines, NLP applications, AI service integration, MLOps, data preparation, deployment, monitoring, testing, and scalable software engineering. Collaborates with engineering, product, and data teams in a distributed environment while supporting responsible, performant, and cost-effective AI development.

Support customer implementations and production deployments of AI-powered engagement solutions. Manage configuration and operational requests, coordinate Customer Success and engineering teams, track milestones, document requirements and changes, troubleshoot issues, support testing and validation, and maintain responsive customer communication. The role also contributes to process improvements and quality service delivery across distributed international teams.

Designs, develops, monitors, and delivers scalable, secure GenAI and machine learning solutions on AWS for a financial-services client. Collaborates with multiple teams to define business metrics, architectural solutions, functional and non-functional requirements, and technical documentation. Records architectural decisions, supports solution delivery, and leads team knowledge sharing and development. Advanced English proficiency is required.

Design, build, deploy, and optimize end-to-end AI systems for cybersecurity applications. Develop production LLM applications and AI agents, manage the full machine learning lifecycle, analyze large-scale unstructured security data, and improve system scalability, reliability, and observability. Collaborate with product, engineering, and security teams to turn AI research into user-facing platform features.

Own and improve the Command|Alert alerting platform, including rule evaluation, anomaly detection, event correlation, reliable Kafka and OpenSearch pipelines, and downstream notifications. Convert security and network telemetry into usable topologies and dependency maps, support LLM-based alert generation, resolve production issues, lead reliability improvements, deliver independent projects, and mentor engineers while shaping technical direction.

Leads Snowflake’s LATAM Solutions Engineering team in Brazil, managing sales engineers while supporting technical sales cycles, demonstrations, proofs of concept, customer presentations, and regional technical-win forecasting. Partners with sales, channel partners, product, engineering, marketing, and services teams to achieve quota goals, identify customer solutions, improve win rates, and expand into new territories. Responsibilities also include recruiting, interviewing, coaching, developing team talent, and advising revenue leadership on sales strategy and product needs.

Leads Citi’s Brazil Treasury and Trade Solutions data and analytics agenda, translating business priorities into analytical opportunities and delivering solutions from concept through adoption. The role applies AI and machine learning to improve commercial performance, supports sales and product teams, drives analytics commercialization, partners with technology and data science teams, and scales global capabilities. It requires strong business judgment, technical familiarity with Python and Snowflake, commercial orientation, cross-functional influence, and advanced English proficiency.

Own product strategy, roadmaps, backlogs, feature development, testing, and performance analysis for mobile games. Collaborate with game leadership and engineering, art, distribution, and data science teams throughout the product lifecycle. Coordinate stakeholders, communicate updates, define product requirements, evaluate partnerships, and use customer insights, benchmarks, and analytics to improve game performance.

Leads a data engineering team responsible for building scalable safety solutions and data platforms. Owns technology roadmaps, architecture decisions, data pipeline design, cloud systems, data quality, monitoring, CI/CD optimization, and enterprise ETL/ELT. Recruits, coaches, and develops engineers while establishing engineering standards, testing practices, reusable frameworks, and reliable batch and streaming architectures supporting analytics and machine learning.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Build and deploy statistical and ML models (especially demand forecasting) to improve sales forecast accuracy. Analyze data, create actionable insights, collaborate with stakeholders and regional teams, set analytics standards, monitor model performance, and deliver dashboards and visualizations for supply chain decision-making.