Top Machine Learning Jobs in Mexico City

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

This role involves leading strategic planning, product innovation, and cross-functional collaboration to enhance pharmacy analytics and drive growth initiatives for CVS Health.

Lead high-impact customer pipelines for Scale's Generative AI unit: design data taxonomies, build pipeline infrastructure, manage global teams, create analytics, partner with Engineering/Product/Ops/Go-to-Market, and report to executives to drive multi-million dollar revenue growth.

Create and review realistic, locale-specific robotics and natural sciences tasks in Spanish (LATAM), apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

Design, build, and maintain production-grade ML systems for large-scale legal NLP datasets. Develop and orchestrate ML/AI agents, prototype solutions, collaborate with cross-functional teams, and work with legal data annotators to improve data quality and drive product-focused outcomes.

As an AI/ML Engineer, design and implement AI solutions, collaborate with teams, build ML pipelines, and ensure quality and compliance in healthcare applications.

The role involves developing and maintaining a robust ML/AI platform, focusing on infrastructure, autonomous agents, CI/CD pipelines, and AI-native tooling, with responsibilities across engineering best practices.

The Senior AI/ML Engineer will architect and develop AI/ML solutions, collaborate with teams, and ensure deployment compliance, especially in healthcare.

The Account Executive will develop and close business opportunities, achieve sales quotas, and collaborate with cross-functional teams within the enterprise market.

Design and implement Azure-based CI/CD pipelines (YAML) with quality gates, automate infrastructure provisioning (ARM/Terraform), manage Kubernetes/OpenShift deployments, integrate tooling (SonarQube, JFrog, Git), script automation tasks, troubleshoot app-service interactions and AAD integrations, and support release and deployment processes.

Build, maintain, and improve programming solutions and AI/ML applications to automate workflows, analyze operational data, and optimize processes. Collaborate with cross-functional teams, maintain operational systems, and document technical procedures to support reliable, scalable operations.

As a Data Scientist at Zicasso, you will analyze complex datasets, develop predictive models, and optimize customer experiences using AI and machine learning techniques, while collaborating with cross-functional teams.

Implement, maintain, and troubleshoot Workday integrations and HCM configuration. Design AI-powered automation and validation for HR processes, build custom reports and dashboards, ensure data quality, collaborate with HR/payroll/talent and external vendors, and support deployments and user documentation.

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.

The Global Privacy Counsel will lead the data protection strategy for TaskUs, ensuring compliance with global privacy laws, overseeing privacy initiatives across product development, managing third-party vendors, and leading the privacy team.

Design, architect, and deliver enterprise-grade generative AI solutions (LLMs) for healthcare. Build end-to-end ML pipelines, scalable cloud deployments, APIs and integrations, prompt engineering, RAG and conversational systems. Ensure responsible AI, privacy compliance, performance optimization, and cross-functional collaboration while documenting architectures and best practices.

Lead technical client engagements in machine learning and AI, shape strategy, influence architecture, and ensure high-quality outcomes for clients by leveraging AWS services.

Lead a team of ML engineers, manage client engagements, and provide strategic advisory on ML architecture while driving pre-sales. Ensure team growth and quality of deliverables.

Design and implement scalable Generative AI architectures, build Python services, integrate LLMs, oversee deployment operations, and evaluate emerging technologies.

The Machine Learning Engineer / Data Scientist will build and deploy machine learning solutions, translating business needs into ML problems, working with data analysis, model development, and productionization tasks.

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.

Design, build, deploy and monitor ML models for fraud detection, credit risk, churn, cross-sell and collections. Handle large datasets, feature engineering, production-grade code, and scalable feature/model pipelines. Collaborate with risk, fraud, engineering and product teams and integrate new data sources to improve model performance in international markets.

Develop, deploy, and monitor production ML models and infrastructure for fraud detection across global markets. Own feature engineering, model lifecycle, scalable pipelines, and collaborate with fraud, product, engineering, and risk teams to improve model performance and decisioning.

Design, train, and refine machine learning algorithms on high-dimensional data to produce practical predictive models. Apply random forests, deep learning, generative modeling, and neural-network techniques to improve NLP and machine perception for real-world applications.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.