Top Machine Learning Jobs in Sao Paulo

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%.

Design, build, validate, and productionise predictive and decisioning models (fraud, risk, onboarding, pricing, CLV). Frame business problems, run experiments and causal analyses, engineer data pipelines, monitor model performance and drift, and integrate models into product workflows while contributing to team tooling and standards.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

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.

Lead horizon scanning and external intelligence to identify AI and disruptive tech opportunities for R&D. Translate trends into actionable briefs, design and run pilots with stage-gates, and scale successful capabilities into production. Influence senior leadership with strategic foresight, build external partnerships, and embed a future-back innovation culture across cross-functional teams to measurably improve R&D outcomes.

The Research Engineer will build training infrastructure and reward pipelines, fine-tune models, bridge RL with LLMs, run fast experiments, communicate findings, and collaborate with the research team at Firecrawl.

Advise clients on antitrust eDiscovery compliance using machine learning and generative AI. Build and validate TAR/AI models (Epiq AI, Relativity, Reveal, Brainspace), craft LLM prompts, draft defensible protocols, train/transfer workflows to review teams, and support client-facing, pre-sales, and thought-leadership activities. Must manage multiple projects and meet deadline-driven schedules.

Drive partner sales strategy and business development for MSPs, paytechs, and fintech partners. Own partnership lifecycle: negotiate agreements, build joint FICO+AI solutions, forecast channel sales, manage pipeline, meet revenue targets, and coordinate cross-functional execution and partner events.

As an AI Operations & Automation Specialist, you will optimize finance and HR operations through automation, AI implementation, and process improvement while ensuring data integrity and supporting various teams in the company.

Lead Felix's global pricing strategy and infrastructure. Define monetization trade-offs, run advanced pricing experiments and elasticity research, partner with Finance on unit economics, and collaborate with Engineering to build automated/ML-driven pricing engines while keeping pricing transparent and user-friendly.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

The Refining and Petrochemical Process Optimization Engineer will optimize manufacturing processes using AI, working with plant personnel and leading project implementations.

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.

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.

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.

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.

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.

Work with client data to develop fraud-detection models: clean and preprocess data, engineer features, build and tune ML models, create scalable pipelines, collaborate with clients and internal teams, communicate results, and contribute technical leadership and product enhancements.