Top Machine Learning Jobs in Sao Paulo

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.

Lead engineering teams in automation and corporate systems, promoting technical excellence and aligning with business objectives. Drive AI adoption for operational efficiency and manage engineering processes.

Data Scientists at Thoughtworks utilize statistics, modeling, and machine learning to provide business insights. Responsibilities include developing strategies, analyzing data, and collaborating with teams to communicate findings.

The Generative AI Engineer role involves tuning AI models, collaborating on project requirements, conducting research, optimizing existing models, and documenting technical specifications.

As a Senior Project Manager, you will lead complex software and AI project deliveries, manage cross-regional teams, and engage with clients to align technology solutions with business goals.

The AI Senior Product Manager at dLocal will define the product vision for AI tools, collaborate with engineering teams, and measure product success to drive AI adoption across the company.

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

As an Applied AI Researcher, you will develop algorithms for diverse data modalities, collaborate on innovations, and lead research dissemination in GenAI.

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.

Lead data science projects integrating expertise in statistics and machine learning to drive business value. Collaborate cross-functionally, ensure data quality, and mentor junior team members.

Lead KYC integrations and AI tools, manage compliance workflows, analyze data for improvements, and collaborate with teams to enhance user experience.

Design and deploy machine learning models for data-driven decision-making, ensuring integration and scalability, while collaborating with cross-functional teams.

Design and implement AI solutions using Azure technologies, focusing on architecture, DevOps, data pipelines, and security compliance while leading AI initiatives.

Design and maintain data pipelines, work on LLM-driven applications, and optimize workflows while collaborating with stakeholders in the BI team.

The role involves managing AI/ML services on AWS and Kubernetes, ensuring reliability through SRE principles, optimizing performance and costs, and leading cross-functional projects.

Lead the design and development of scalable ML systems, oversee technical feasibility, and drive innovation through strategic alignment with organizational goals.

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

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

The role involves designing robust ML systems, collaborating with teams, and leading functional streams, ensuring delivery and strategic alignment in ML applications.

Lead the design and development of scalable machine learning systems, managing technical strategies, fostering collaborative environments, and ensuring impactful outcomes for clients.

Lead a team of ML engineers and architects to deliver AI/ML solutions on AWS, focusing on strategy, technical innovation, and operational excellence.

The Senior Machine Learning Engineer is responsible for designing, building, and managing ML applications and systems, ensuring functionality, scalability, and performance while collaborating with data scientists and mentoring team members.

Design and develop AI/ML solutions, lead RAG architecture implementation, automate deployment, and perform advanced data analysis.