Top Machine Learning Jobs in Mexico

Design and implement Industry 4.0 solutions (IoT, AI/ML, robotics, AR/VR, 3D printing, Big Data) to optimize operational and office processes. Integrate with ERP (SAP), MES, PLCs, robots and databases, ensure SOX compliance, support cloud and on-prem deployments, and develop BI/analytics solutions.

Architects and deploys enterprise-scale machine learning infrastructure, including pipelines, model serving, monitoring, governance, CI/CD, registries, versioning, lineage, and automated retraining. Leads deployment of forecasting, optimization, and NLP models using Azure Databricks, MLflow, Kubernetes, and Docker. Drives MLOps best practices, hybrid or multi-cloud integration, observability, audit compliance, and team mentorship.

Leads Stripe’s Credit Risk machine learning engineering team, setting technical and product strategy for systems that detect and mitigate credit risk. Owns outcomes including credit losses, profitability, detection quality, and user experience. Oversees delivery of reliable models and decision systems, partners cross-functionally on priorities, and recruits, develops, and manages machine learning engineers while contributing to broader engineering leadership.

Mercor is offering $5 million in grants for independent researchers, academics, PhD students, and small teams conducting frontier AI safety research. Focus areas include misalignment, sandbox escape, evaluation awareness, interpretability, oversight and control, and red-teaming. Funding may support researcher time, API credits, and conference attendance. Applicants should submit a focused expression of interest describing their team, background, research proposal, outputs, impact, timelines, and resource needs.

Lead backend architecture for a large-scale distributed platform, designing scalable APIs, microservices, integrations, data workflows, and cloud infrastructure. Drive decisions around performance, availability, security, observability, and resilience while collaborating with product, cloud, and AI teams. Provide technical leadership, mentor engineers, resolve critical incidents, and improve production systems. Requires extensive backend development experience, distributed systems expertise, cloud experience, and proficiency in TypeScript, Python, or Java.

Own and improve the Command|Alert alerting platform, including rule evaluation, anomaly detection, event correlation, reliable Kafka and OpenSearch pipelines, and downstream notifications. Convert security and network telemetry into usable topologies and dependency maps, support LLM-based alert generation, resolve production issues, lead reliability improvements, deliver independent projects, and mentor engineers while shaping technical direction.

Owns deployment, configuration, and reliable operation of end-to-end machine learning pipelines on Databricks. Responsibilities include orchestrating Jobs, managing Databricks Asset Bundle deployments across environments and countries, administering MLflow and Unity Catalog model lifecycles, monitoring compute costs and observability, and diagnosing production incidents with development and platform teams. Requires advanced Python, SQL, Delta, Unity Catalog, CI/CD, and infrastructure-as-code experience.

Develop, design, and troubleshoot software components as part of an agile team. Write secure, high-quality code, apply SDLC and automation tools, analyze data to identify issues, and contribute to system stability and scalability while learning cloud, AI/ML, and mobile technologies.

Leads customer-facing computational biology and bioinformatics engagements from strategy through delivery. Develops scalable multi-omics analyses, integrates pipelines, statistical models, machine learning, and heterogeneous datasets, and generates insights for drug discovery, biomarker identification, indication selection, and patient stratification. Manages project scope, resources, milestones, governance, stakeholders, and delivery challenges while supporting proposals, scientific presentations, thought leadership, and mentoring.

Designs and delivers production-ready AI, machine learning, NLP, and generative AI solutions for scientific knowledge discovery and decision support. Builds LLM workflows, RAG systems, retrieval and ranking capabilities, and scalable AI pipelines using scientific metadata and knowledge assets. Establishes evaluation and monitoring frameworks, writes production Python, collaborates with engineering teams, and provides technical leadership and mentoring.

Design and deliver production-ready AI, machine learning, NLP, generative AI, and LLM solutions for scientific knowledge discovery and decision support. Build RAG, retrieval, ranking, recommendation, and evaluation systems using large-scale content datasets. Integrate metadata and knowledge assets, write Python code, monitor system quality, and collaborate with engineering teams. Provide technical leadership and mentoring.

Leads a 45-person employee organization and 50 contractors responsible for USAA’s telematics application strategy, architecture, engineering delivery, vendors, budgets, security, compliance, and modernization. Oversees cloud, mobile, data, AI, machine learning, IoT, and connected-vehicle capabilities supporting insurance pricing, underwriting, claims, fraud detection, and member engagement. Partners with business and technology executives on roadmaps, technical debt reduction, SDLC controls, risk management, and ecosystem relationships.

Leads AI and data science strategy across machine learning, NLP, search, recommendation, experimentation, and generative AI. Manages technical teams, develops AI talent, oversees systems from experimentation through production, establishes evaluation and responsible AI frameworks, and partners with product, technology, and business leaders to shape roadmaps and deliver measurable customer and business outcomes.

Leads AI and data science strategy across machine learning, NLP, search, recommendation, experimentation, and generative AI. Manages technical teams, develops talent, and oversees AI systems from experimentation through production. Builds LLM, RAG, semantic search, and knowledge-discovery solutions; establishes evaluation frameworks, quality metrics, experimentation practices, and responsible AI standards. Partners with product, technology, and business leaders to shape roadmaps and deliver measurable customer and business outcomes.

Develop and maintain pricing models for accident and health products across Latin America using Radar. Analyze internal and external data, prepare rate reviews, monitor pricing variables and profitability, improve pricing standards, and ensure data quality. The role partners with IT and Commercial teams, supports data cleanup and restructuring, and provides pricing insights and monitoring recommendations to the A&H Manager.

Analyzes human-generated data quality across AI programs using SQL, Python, statistical methods, and hands-on annotation review. Investigates quality issues, identifies failure patterns, measures impact, and develops recommendations. Supports quality pipelines, automated validations, rubrics, gold sets, calibration exercises, reporting, and dashboards. Collaborates with quality engineering, operations, delivery, and engineering teams to improve annotation design, training, tooling, and quality controls.

Analyzes veterinary and customer data to support hospital site selection, acquisitions, revenue optimization, forecasting, pricing, predictive modeling, and executive decision-making. Builds integrated datasets, statistical models, experiments, and interactive BI dashboards using SQL, Python or R, and visualization platforms. Communicates quantitative insights and recommendations to technical and business stakeholders.

Own complex AI and data projects from customer discussions through delivery, translating needs into execution plans and coordinating large networks of domain experts. Lead and develop project associates, manage customer relationships, analyze performance data, improve quality and efficiency, and identify expansion opportunities. Collaborate with Operations, Engineering, and Machine Learning teams while overseeing projects with substantial revenue and contributor scale.

Execute large-scale projects for AI labs and technology companies, managing expert networks, multimillion-dollar portfolios, delivery quality, operational issues, customer communication, and process improvements. The role requires analytical problem-solving, prioritization, ownership, and comfort with ambiguity while collaborating across operations, engineering, and machine learning teams. Candidates may be recent graduates and will work remotely from Mexico City before transitioning to an in-person model.

Design and build production-ready machine learning, NLP, and generative AI systems for scientific discovery, knowledge extraction, semantic search, recommendation, summarization, and decision support. Work with large-scale scientific content and heterogeneous datasets, develop and evaluate models, fine-tune LLMs, create scalable Python pipelines, and support deployment, monitoring, retraining, and optimization. Collaborate with engineering, product, research, UX, analytics, and domain experts to deliver measurable user value.

Build and operate production machine learning and data pipelines on Databricks. Responsibilities include orchestrating Jobs workflows, managing Databricks Asset Bundle deployments across environments and countries, administering MLflow and Unity Catalog model lifecycles, monitoring compute costs and observability, and diagnosing runtime incidents. The role requires advanced Python, SQL, Delta, Unity Catalog, CI/CD, Azure DevOps, Git, infrastructure as code, and Databricks platform expertise.

Supports risk strategy for one to two credit portfolios by monitoring profitability and risk KPIs, preparing analyses, documenting recommendations, coordinating strategy rollouts, and implementing risk controls. Uses SQL, R, or Python for data extraction, manipulation, modeling, forecasting, and statistical analysis. Partners with Data Science on machine learning and AI models and applies A/B test recommendations to credit policies while communicating findings to stakeholders.

Supports development, testing, validation, and operationalization of AI-driven data pipelines for private-markets analytics. Responsibilities include tuning AI/ML models, structuring heterogeneous data from SQL, documents, and Excel, conducting backtests and scenario analyses, integration testing, documenting requirements, automating analytical workflows, and writing advanced SQL queries. The role requires foundational Python, data-quality discipline, collaboration, and quantitative analytical skills.

Leads a global multidisciplinary AI/ML team while remaining hands-on in architecting, developing, deploying, evaluating, monitoring, and governing production AI solutions. Applies traditional machine learning, deep learning, LLMs, RAG, and agentic AI to complex business problems. Partners with product, engineering, data, platform, and business teams; establishes responsible AI practices; and communicates technical strategy, risks, milestones, and outcomes to senior stakeholders.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.