Top Machine Learning Jobs in Sao Paulo

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.

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.

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.

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.

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.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Owns financial data and BI product strategy across Quote-to-Cash and Order-to-Cash processes. The role identifies operational gaps, translates finance and revenue lifecycle needs into PRDs and roadmaps, validates pricing and contract data, and develops dashboards and AI/ML analytics opportunities. It partners with Sales, Legal, Accounting, Finance, Product, and data engineering teams to improve downstream reporting and decision-making.

Lead the full product lifecycle by owning roadmaps, translating business priorities into executable plans, defining features with engineering and UX/UI teams, and driving timely delivery. Conduct user research, establish product metrics, lead client demos, support go-to-market positioning, and align product development with broader innovation goals. Manage cross-functional teams, including ML engineers, while iterating products based on user feedback.

The Senior Data Developer extends and maintains existing code repositories, builds ETL/ELT pipelines, integrates enterprise data from BigQuery and SAP, and designs database structures. Depending on expertise, the role also supports front-end and backend application development, scheduled jobs, and machine learning for computer vision and forecasting. The developer follows Git best practices, communicates clearly, and collaborates with distributed cross-functional teams across supply chain, manufacturing, logistics, and procurement initiatives.

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.

Conduct exploratory analysis, statistical modeling, experimentation, forecasting, and machine learning for marketplace and pricing systems. Analyze supply, demand, elasticity, conversion, and pricing behavior; build predictive models; improve data quality and analytical foundations; contribute to production ML; and communicate actionable recommendations to Product, Engineering, Pricing, and business stakeholders.

Lead the development, deployment, governance, monitoring, and continuous improvement of enterprise machine learning solutions. Own MLOps architecture, Databricks-based pipelines, feature stores, model serving, versioning, CI/CD, infrastructure as code, observability, testing, and production support. Partner with data scientists, establish engineering standards, conduct code reviews, and ensure ML systems are scalable, reproducible, auditable, and secure.

Design, deploy, and optimize large-scale recommender systems and machine learning pipelines. Build recommendation models using architectures such as Wide & Deep, Two-Tower, Transformer, embedding-based, autoencoder, and sequential models. Process data with Databricks and Spark, integrate models into production, monitor performance, conduct experimentation and A/B testing, and collaborate with engineering, data science, and business teams.

Build and deploy LLM-powered agents, workflows, and integrations using established templates, pipelines, and guardrails. Develop evaluation suites, integrate with business systems and data sources, instrument cost and quality telemetry, and collaborate with business users to iterate on bounded use cases. The role requires strong Python and TypeScript skills, production experience with LLM applications, API craftsmanship, and expertise in prompt and context engineering.

Build and deploy LLM-powered agents and workflows, create comprehensive evaluation suites, integrate AI systems with enterprise data sources, instrument cost and quality telemetry, and collaborate with business users to rapidly deliver production systems within defined guardrails.

Supports the Solutions Engineering team by creating SQL demonstrations, exploratory analyses, queries, reports, visualizations, prototypes, and proof-of-concepts using public and customer datasets. Assists with environment setup, task automation, cloud infrastructure, and technical community initiatives such as events, webinars, training, and content creation. Collaborates with technical leadership while gaining practical experience in data science and analytics.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Full-cycle enterprise sales role focused on acquiring and expanding accounts in Brazil. Evangelize Replit, run demos and hackathons, manage deals and CRM (HubSpot), collaborate with product/engineering, negotiate contracts, support customer adoption, and gather feedback. Travel up to 25%.

Leads data science strategy across Mexico and Brazil, manages and mentors a distributed team, and remains hands-on building and deploying machine learning models. Establishes modeling standards, infrastructure, delivery practices, and technical roadmaps while partnering with credit leadership on underwriting, valuation, impact measurement, and P&L analysis. Requires strong applied machine learning expertise, prior data science leadership, business communication skills, and understanding of credit or underwriting.