Top Machine Learning Jobs in Brasilia

The Advisory Solutions Consultant supports LATAM sales for Strategic and Enterprise accounts by aligning customer requirements with SailPoint solutions, providing technical expertise, and delivering product demonstrations.

As a Data Engineer, you will develop and maintain ETL processes using Python on Azure Synapse, design data warehouses, and ensure data quality and compliance through collaboration with cross-functional teams.

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.

Manage GPU capacity and compute resources across cloud providers, develop data models and dashboards, analyze performance, and enhance cloud infrastructure efficiency initiatives.

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 Senior Product Owner will lead the Anti-Fraud Squad, enhancing security infrastructure, managing a technical backlog, and leveraging data for fraud prevention. Responsibilities include strategy, data analysis, and collaboration with engineering and data science teams.

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.

Responsible for managing a team of cloud engineers, overseeing technical tasks, engaging with clients, mentoring team members, and ensuring technical accountability on projects.

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.

As an Analytics Engineer, you'll refine data, build models, enable self-service analytics, and collaborate with various teams to improve customer insights.

Drive machine learning initiatives by collaborating with teams to produce AI solutions, develop models, and communicate with stakeholders. Responsible for building scalable cloud-native applications and pipelines.

This role involves designing and implementing scalable ML and AI solutions, mentoring team members, and driving technical strategy in cloud environments.

Develop and evaluate scalable deep learning algorithms, collaborate on generative modeling, and improve data processing pipelines to enhance understanding of neural mechanisms.

Architect, build, and deploy low-latency, high-throughput ML systems across the full ML lifecycle: scalable feature pipelines, training/tuning of tree and deep models, production packaging/serving, MLOps automation, monitoring for drift, and rigorous A/B experimentation to drive business metrics.

As a Machine Learning Engineer/Data Scientist, you'll build and deploy ML solutions, collaborate with stakeholders, and analyze data, focusing on achieving measurable business impact.

The Product Owner will manage the product backlog, translate business needs into user stories, ensure product alignment within Agile teams, and drive continuous improvement of product outcomes, especially in AI/data-driven products.

The Machine Learning Engineer will develop distributed ML systems, collaborate on model deployment, optimize feature extraction, and enhance ML capabilities.

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.

The Machine Learning Architect will design scalable AI and ML solutions, lead architecture development, implement MLOps, and collaborate with teams for effective machine learning capabilities.

The role involves developing and supporting data processing pipelines, collaborating with ML Engineers, and improving infrastructure while mentoring team members.

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

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.

Design and build simulation environments for robotics research, implement RL algorithms, and evaluate policies for stability and transfer potential. Collaborate with teams and document results.

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.