Top Machine Learning Jobs in Mexico

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.

Support and optimize EDA tools, analog circuit simulation, verification flows, and design methodologies for HBM and DRAM memory development. Build scalable software and automation, integrate tools with EDA vendors, troubleshoot complex engineering challenges, and develop AI-assisted automation for simulation and verification. Collaborate with global design, verification, layout, and process engineering teams while providing technical leadership and mentorship.

Manage and improve hardware service delivery operations in Mexico City, including warehouse, inventory, reverse logistics, repair, transportation, fulfillment, supplier governance, and customer escalations. Establish operational KPIs, analyze trends, manage service-level agreements, drive continuous improvement, ensure compliance, and coordinate training and competency development. The role requires close collaboration with customers, logistics providers, technical teams, finance, sourcing, and internal stakeholders, with responsibility for reliable service execution and accountability across operational processes.

Develop and deploy data solutions using Palantir Foundry and AIP for client engagements. Lead technical teams, coach team members, analyze system interactions, build applications with large datasets, and apply data engineering and analytics to deliver client outcomes. The role also includes managing client relationships, planning and executing projects, encouraging technology adoption, resolving stakeholder conflicts, and driving continuous improvement.

Leads the development and deployment of data solutions using Palantir Foundry and AIP. Manages engineering teams, coaches team members, oversees project planning and execution, analyzes system interactions, manages client relationships, and drives innovation. The role applies software engineering, data engineering, analytics, and AI/ML techniques to deliver scalable client solutions while maintaining technical and professional standards.

Leads the development and deployment of software and data solutions using Palantir Foundry and AI/ML technologies. Responsibilities include data engineering, analytics, system improvement, client account management, project ownership, team leadership, coaching, stakeholder engagement, and continuous improvement. The role requires building scalable applications with large datasets and delivering customer-focused solutions.

Support CAD tools and flows for HBM design, collaborate with design teams, and engage in mixed-signal IC design processes.

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.

Leads AI-enabled risk modeling services, setting strategy, managing client relationships, overseeing machine learning and predictive analytics solutions, and guiding end-to-end delivery. The role includes model governance, feature engineering, performance evaluation, innovation, cross-functional collaboration, team coaching, thought leadership, and business development across risk modeling engagements.

Leads financial statement audits for digital assets and financial services clients, evaluating regulatory compliance, governance, risk management, and internal controls. Applies advanced auditing and technical accounting expertise to identify material misstatements and issue audit reports. Directs complex engagements, coaches diverse teams, manages client relationships, communicates findings, and reinforces professional, independence, and ethical standards. The role also involves data analysis and exploring AI and machine learning platforms to improve processes.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

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.

Design and build scalable distributed services, SaaS integrations, analytics using natural language processing and machine learning, data visualizations, and full-stack solutions. Collaborate with customers, product managers, operations, and engineering teams in an Agile environment while owning quality and delivery. Required skills include Java, PHP or Scala, JavaScript, web technologies, Angular or Node.js, and SQL/MySQL. The role follows a hybrid schedule with three office days weekly.

Build scalable SaaS systems for ingesting, processing, and aggregating experience data. Contribute to integrations with Slack, Salesforce, and Tableau; develop analytics tools using natural language processing and machine learning; create dashboard and reporting widgets; and collaborate with customers, product managers, and engineering teams in an Agile environment.

Conducts strategic data analysis and statistical modeling on banking data to identify insights, improve data quality, and support business decisions. Develops data visualizations, trains machine learning models, defines analytical requirements, and creates project plans. Partners with business and IT teams to solve complex system issues, implement changes, provide application support, and communicate risks and recommendations. Requires Python, analytics, statistics, Microsoft proficiency, and strong communication skills.

Develop, validate, deploy, and support analytics for GE Aerospace’s FlightPulse product. Responsibilities include exploratory analysis, data quality assessment, data preparation, model validation, production support, customer implementation, troubleshooting, and communicating insights to technical and non-technical stakeholders. The role collaborates with product, engineering, software, domain, and customer-facing teams to translate business needs into reliable analytical solutions and improve product performance and customer outcomes.

Executive leader responsible for global digital transformation across supply chain, quality, EHS, sustainability, data, AI, and automation. Defines enterprise strategy, governs data architecture, modernizes SAP and ERP ecosystems, harmonizes processes, and drives intelligent workflow adoption. Partners with senior business leadership, manages global teams and budgets, oversees vendors, and delivers measurable improvements in operational KPIs, cost, resilience, and technology adoption.

Develops and deploys statistical, machine-learning, and AI solutions using manufacturing data to improve quality and operational KPIs. The role requires manufacturing analytics experience, strong Python or R and SQL skills, dashboarding capabilities, advanced English, and onsite work in Monterrey for a 16-week contract.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

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.