Top Machine Learning Jobs in Sao Paulo

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.

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.

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.

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.

Lead LATAM partner ecosystem strategy and execution across GSIs, OEMs, acquirers, payment processors, hyperscalers, and ISVs. Drive partner-sourced and partner-influenced revenue, joint GTM plans, partner recruitment/onboarding, enablement, and performance governance using BI and PRM tools. Collaborate with sales, marketing, product, and customer success to scale Feedzai's footprint in LATAM.

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

Drive development of AI products focused on Knowledge Graphs and LLMs, design architectures, integrate data, and build APIs.

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.

Train and evaluate machine learning models, build predictive models, design feature pipelines, and integrate models into production systems.

The Research Engineer will design and build evaluation systems to measure data quality for Firecrawl's web data extraction, creating metrics and benchmarks, and collaborating on model improvements.

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.

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.

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

Lead the architecture and implementation of machine learning solutions, ensuring scalable and secure environments for data teams while collaborating with clients and various stakeholders.

The Principal AI/ML Architect leads client engagements, advising on ML architecture and strategies, overseeing ML systems, and ensuring technical quality for diverse customer needs with a focus on AWS technologies.

The AI/ML Engineering Manager will lead a team of ML engineers, manage client engagements, shape architecture, and drive pre-sales while ensuring team growth and performance.

Join the Advanced Accelerator Training Program to master AI/ML skills, engage in real projects, and prepare for impactful roles. Requires 4+ years of ML experience and proficiency in Python and relevant frameworks.

The Engineering Matcher connects clients with technical talent by analyzing resumes, conducting interviews, and maintaining a candidate pool while collaborating with stakeholders.

Seeking a Software Engineer with 5+ years in AI/ML to design and implement scalable web APIs and applications, focusing on generative AI solutions, while engaging cross-functional teams and mentoring junior developers.

Develop and implement LLM workflows, collaborate with teams on AI/ML solutions, ensure quality delivery using CI/CD practices, and support technical duties.

The Machine Learning Engineer will develop RAG models, optimize machine learning systems, collaborate with clients, and lead AI projects for effective solutions.

Own product direction for ML platform capabilities across the end-to-end ML lifecycle. Set vision and roadmap, define standards and tooling, drive adoption with data science and engineering teams, deliver templates/CLIs/workflows, and improve model deployment, observability, and operational practices to enable scalable, reliable ML solutions.

As a Machine Learning Solutions Architect, you will design and implement data solutions, provide thought leadership, and ensure scalable deployment of machine learning models.

Build and maintain production ML infrastructure: deploy, monitor, and scale models; create automated end-to-end pipelines; implement CI/CD for model releases; establish monitoring/logging/alerting; optimize system architecture for reliability, scalability, and cost; collaborate with data scientists and engineers to operationalize models.

Design and implement recommender systems, manage ML pipelines, optimize models, and collaborate with teams to improve customer engagement.