Top Machine Learning Jobs in Mexico

Design, build, and own end-to-end unit-economics frameworks linking acquisition, multi-product lifecycles, reimbursement, and costs. Develop LTV/reimbursement models, attribution and pricing experiments, causal uplift models for churn interventions, and Monte Carlo simulations to stress-test business scenarios. Partner with finance, marketing, and operations and deploy production-grade models influencing strategic decisions.

Leads data engineering, Palantir Skywise architecture, and AI solution development for Airbus manufacturing and operational environments. Builds scalable pipelines, analytical workflows, machine learning and generative AI applications, and enterprise data foundations. Oversees Alteryx and Tableau platforms, establishes governance, mentors technical teams, translates business challenges into data products, and supports AI Center of Excellence initiatives. The role is onsite 60% across several U.S. locations and requires approximately 10% travel.

Conduct offensive, AI-driven red-team assessments by validating automated attacker decisions, resolving authentication and CAPTCHA challenges, guiding reconnaissance, classifying attack data, and providing feedback to improve security models. The role requires maintaining context across multiple client engagements, making safety-critical judgments, and documenting technical decisions. Candidates need at least one year of relevant experience through work, internships, classes, or projects, plus authorization to work in Mexico without sponsorship.

Leads digital enablement projects from planning through delivery, managing scope, budgets, timelines, risks, resources, reporting, and change management. Develops technical automation solutions for business processes, evaluates digital tools, supports continuous improvement, and presents project progress to leadership. Partners across functions, manages multiple initiatives, resolves complex problems, and ensures projects meet quality, control, deadline, and value objectives.

Lead strategy, roadmap, architecture collaboration, and lifecycle management for an observability platform supporting retail commerce infrastructure. Define telemetry collection, processing, analytics, visualization, AI-driven anomaly detection, predictive monitoring, root cause analysis, and automated operational responses across hardware, edge, cloud, and application layers. Partner with engineering and data teams to deliver scalable platform capabilities and machine learning-powered operational insights.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

Leads the build-out and operation of XDOF’s LATAM robotics data-collection network in Mexico or Brazil. Responsibilities include expanding collection venues, sourcing partnerships, recruiting and managing field teams, deploying and maintaining hardware, optimizing operational efficiency, monitoring data quality, and converting market learnings into reusable SOPs and playbooks.

Leads analytics-focused consulting engagements for global clients, developing data strategies, analytical solutions, and business recommendations. Manages senior stakeholder relationships, leads project teams, presents insights, collaborates with Mastercard partners, and mentors junior consultants. The role applies quantitative and qualitative techniques using large datasets, AI, machine learning, BI platforms, and analytics technologies to deliver measurable client impact.

Build and support self-service business intelligence and analytics solutions. Develop data models, information marts, semantic layers, and end-to-end pipelines using Databricks and Azure services. Collaborate with data engineers, analysts, data scientists, and business stakeholders to gather requirements, deliver actionable insights, optimize data processing, and ensure data warehouse quality, controls, testing, and scalability. Provide lakehouse support, establish analytics standards, conduct research, and communicate technical concepts to nontechnical users.

Lead quality engineering for AI-powered products by designing scalable test automation across API, UI, integration, end-to-end, performance, and AI validation. Define quality strategies, metrics, coverage, and CI/CD quality gates; evaluate AI-assisted testing technologies; drive shift-left practices; and mentor developers and engineers on maintainable automation.

Lead the architecture, development, and production delivery of AI software products and platform capabilities. Collaborate across software development, data science, product management, and domain teams to apply generative AI, agentic systems, and machine learning. Drive scalability, reliability, security, testing, observability, and operational excellence while influencing technical direction across teams. Mentor developers, evaluate emerging technologies, and align engineering solutions with customer and business needs.

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.

Build and own production AI features serving millions of students. Design debuggable agent, tool, and orchestration systems; establish evaluation practices; choose between LLMs, classic machine learning, and deterministic solutions; make defensible architecture decisions; and improve engineering standards through reviews and technical leadership.

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.

Owns deployment, orchestration, configuration, and end-to-end operation of machine learning pipelines on Databricks. Responsibilities include managing Databricks Jobs and Asset Bundles across environments and countries, maintaining models and artifacts in MLflow and Unity Catalog, monitoring compute costs and observability, and diagnosing production incidents. Requires advanced Python, SQL, Delta, Unity Catalog, CI/CD, and infrastructure-as-code experience.

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.

Own employee experience products from discovery through delivery, adoption, measurement, and iteration. Define product strategy and roadmaps, connect data and workflows, develop governed AI-enabled and agentic experiences, and partner with People, engineering, data, security, and legal teams. Ensure privacy, permissions, evaluation, human accountability, and user trust while driving adoption and organizational change.

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.

Designs and maintains Coveo data ingestion and indexing pipelines, integrating enterprise data sources and configuring metadata, schemas, mappings, and enrichment. Optimizes search relevance using query pipelines, ranking strategies, and machine learning. Monitors analytics and platform performance, troubleshoots synchronization issues, supports data governance, and contributes to AI Search, Generative AI, and knowledge management initiatives. Partners with engineering, product, and business teams to improve enterprise information access.

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.