Top Machine Learning Jobs in Mexico City

Leads strategic technical engagements for complex ThousandEyes customers, providing network observability architecture, executive advisory, advanced escalations, adoption optimization, and crisis management. Serves as a technical liaison to Product and Engineering, mentors TAMs, develops technical content and training, manages cross-functional programs, influences product improvements, and supports hiring and onboarding.

Develops and maintains analytics solutions, datasets, pipelines, dashboards, and reporting frameworks supporting commercial performance. Performs advanced analysis of patient, HCP, payer, and commercial data using statistical and machine learning techniques. Translates business questions into reliable analytical assets and actionable insights, improves workflow automation and data quality, and collaborates with U.S.-based stakeholders to support global commercial analytics priorities.

Reviews live dashcam videos to identify safety risks and annotates data according to established guidelines. Ensures accurate, timely delivery against SLAs while contributing to improved annotation procedures and team best practices. Collaborates with Research and Development teams to create high-quality training datasets for machine learning and artificial intelligence models, and supports Product experiments focused on risk identification and mitigation. Requires rotational day, evening, overnight, and weekend shifts in a 24/7 operation.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Lead the evaluation, training, fine-tuning, and production integration of machine learning and large language models. Design evaluation pipelines, prompt engineering frameworks, benchmarking tools, and AI performance monitoring. Collaborate with product, engineering, and security teams to deploy safe, scalable AI features while optimizing Elasticsearch-backed data systems and assessing commercial and open-source models.

Designs electrical system architectures and equipment for modular and hybrid data center solutions. Reviews RFPs and customer requirements, develops CAD models and electrical design documents, specifies power equipment, coordinates with multidisciplinary teams and customers, and supports technical proposals. The role requires expertise in electrical power distribution, UPS, DC systems, low- and medium-voltage systems, diesel generators, applicable codes, and short-circuit and protection coordination studies.

Develops and executes joint channel marketing strategies with major partners, aligning go-to-market plans to sales targets. Responsibilities include managing partner relationships, campaigns, marketing assets, budgets, performance reporting, training, events, brand compliance, customer advocacy, and cross-functional collaboration. The role analyzes market and channel data, influences partner leadership, and drives sell-in and sell-through growth across multiple marketing channels.

Conducts strategic data analysis and machine learning activities using banking data to identify insights, improve data quality, and support business decisions. Develops analytics initiatives, models, visualizations, and recommendations; defines requirements and project plans; supports system changes and business users; evaluates risks and communicates findings between business and technology teams.

Implement and maintain CI/CD pipelines for application code and AI models, automate cloud infrastructure through Infrastructure as Code, manage Docker containers and Kubernetes orchestration, monitor production AI models and infrastructure performance, and administer AWS environments for scalable, stable, and efficient workloads.

Analyzes banking data to identify insights, improve data quality, and support strategic business decisions. Develops analytical displays, statistical models, and machine learning solutions; consults with business and technology stakeholders on requirements and system issues; leads changes from requirements through implementation; and communicates risks, recommendations, and business impacts. Provides operational support while maintaining compliance, risk management, and data quality standards.

Develops advanced analytics and graph-based intelligence solutions to identify patterns, anomalies, relationships, and business or risk insights. Responsibilities include designing knowledge graphs, building data preparation pipelines, applying machine learning and AI methods, validating model outputs, and ensuring data governance. The role also coordinates operational delivery, risk management, project milestones, reporting, and continuous improvement while collaborating with engineering teams and business stakeholders.

Designs AI/ML architecture solutions aligned with business and technical requirements, evaluates technologies and frameworks, provides architectural guidance, and supports hiring and assessment of AI professionals.

Build and operate production AI capabilities across an enterprise platform, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, cost controls, and latency optimization. Ship reliable, measurable, safe AI features, create evaluation frameworks, monitor behavior, and collaborate with backend, platform, product, security, and client teams to deliver practical business value.

Reviews real-time transactions and customer behavior for fraud and abuse, investigates suspicious activity, blocks risky users, clears false positives, and handles escalations. The analyst identifies fraud trends, analyzes account patterns, supports machine learning and detection improvements, collaborates with cross-functional teams, maintains fraud expertise, and meets case-resolution SLAs.

Own global fraud detection strategy across markets and fraud vectors. Lead fraud infrastructure, rules, machine learning models, AI automation, risk signals, case management, model governance, and performance monitoring. Identify emerging threats and improve detection through data analysis, automation, and cross-functional collaboration. Establish KPIs, ensure explainability and human oversight, and scale 24/7 fraud coverage across regional operations.

Serves as the operational link between XPENG headquarters and its dealer network. Responsibilities include dealership audits, compliance evaluations, on-site coaching, training delivery, multimedia content creation, KPI analysis, customer experience monitoring, and improvement planning. The role requires strong communication, automotive knowledge, resilience, frequent travel, and confidence engaging with dealer teams and presenting on camera.

Source and develop data partnerships for AI training use cases. Translate customer needs into sourcing strategies, identify and qualify data providers, evaluate provenance and rights, coordinate diligence and samples, structure commercial agreements, and move partners through contracting, activation, and handoff. The role also builds scalable sourcing workflows, maintains partner pipelines, supports customer deals, and provides market intelligence across commercial, technical, legal, product, and operations teams.

Lead Protege’s DataLab research strategy and team, focusing on AI training data quality, model evaluation, synthetic data, privacy-preserving methodologies, and benchmarking systems. Partner with Product, Engineering, GTM, customers, and leadership to translate ambiguous technical questions into experiments, frameworks, and actionable recommendations. Establish rigorous, reproducible research processes, publish findings, influence product and customer strategy, recruit researchers, and represent the company across the AI ecosystem.

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.

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.

Leads engineering standards, architecture governance, platform strategy, technical quality, security and compliance engineering, and production AI governance across CSL Plasma’s digital products. Manages cross-domain technical teams, sets cloud and integration direction, owns shared engineering platforms and data architecture, and ensures reliable, scalable, regulated software delivery. The role also drives AI-assisted development, production AI operations, engineering metrics, talent development, and alignment with enterprise architecture and cybersecurity.

Lead four product managers and own PayJoy’s end-to-end onboarding strategy across digital and in-store channels. Drive application, direct-to-consumer, enterprise, and offers/risk product areas; optimize conversion and acquisition costs; guide global versus localized solutions for new markets; ensure regulatory compliance; and collaborate with Engineering, Design, Data, Risk, and Operations to deliver scalable consumer fintech experiences.

Owns end-to-end technical program delivery for launching and scaling financial products across LATAM and South Africa. Coordinates Product, Engineering, Legal, Risk, Design, Operations, vendors, and country teams; manages roadmaps, dependencies, testing, launch readiness, compliance documentation, production operations, and post-launch optimization. Builds repeatable launch playbooks, drives UAT and pilot rollouts, tracks risks and defects, and provides structured status updates to leadership.

Own credit performance for Tilt’s Mexico cash advance product, including credit policy, loan pricing, eligibility, unit economics, experimentation, and portfolio monitoring. Partner with data science to productionize risk models, monitor delinquency and vintage performance, and drive improvements across Product, Engineering, Collections, Finance, BI, and Marketing. The role requires strong consumer credit risk expertise, knowledge of Mexico’s lending market, analytical modeling skills, and executive communication.