Top Machine Learning Jobs in Mexico City

Own and optimize PayJoy’s AI-powered telesales channel, including voice bot operations, agent deployment, vendor integration, customer journey improvements, and channel experimentation. The role establishes quality and compliance standards, analyzes customer conversations, tracks KPIs, and collaborates with Marketing, Product, Product Marketing, and Analytics to improve conversion, retention, and engagement.

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.

Lead post-sale technical and commercial engagement for strategic Salesforce Data Cloud and Agentforce customers. Develop consumption, adoption, enablement, and mutual success plans; guide architecture, governance, integrations, and change management; identify risks; and accelerate platform usage, customer value, and account growth. Build executive relationships, facilitate discovery and workshops, coordinate internal sales, engineering, product, services, and support teams, and communicate product updates. Serve as a trusted technical advisor across CRM, data architecture, analytics, AI, and machine learning.

Lead the Content Foundations engineering team and evolve Scribd’s high-scale document ingestion platform into a real-time, AI-native system. Responsibilities include setting technical strategy, integrating AI and ML capabilities, overseeing distributed data infrastructure, ensuring security and operational reliability, managing the roadmap, hiring and developing backend and AI data engineers, and partnering with product and engineering leaders across the organization.

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.

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 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.

Supports consulting projects by collecting and analyzing complex datasets, building data pipelines and architectures, developing statistical and machine learning models, and creating data-driven applications. The role integrates AI techniques such as OCR and embeddings, produces visualizations and client presentations, identifies analytics opportunities, and translates business needs into practical technical solutions. It requires collaboration with consultants, clients, and data scientists in a hybrid Mexico City environment.

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.

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.

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.

Own sales analytics and forecasting as the trusted source of truth for sales leadership. Build predictive and machine learning models to assess deal close likelihood and slippage, maintain Salesforce and Tableau dashboards, improve data governance, and translate complex business questions into actionable GTM recommendations. Partner with Sales, Marketing, and Finance on reporting standards, strategic planning, QBRs, and executive insights.

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.

Business Analyst supporting enterprise loyalty SaaS implementations for global brands. Responsibilities include client discovery, requirements analysis, KPI definition, business case development, loyalty strategy, platform configuration, implementation coordination, support, training, documentation, and stakeholder communication. The role also contributes to product innovation, marketing collateral, and go-to-market strategy. Candidates need up to three years of client delivery or enterprise SaaS implementation experience, strong analytical and communication skills, and fluency in English, Portuguese, and Spanish.

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.

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.

Leads product growth and digital insights for a business application, owning local product strategy, roadmap prioritization, discovery, requirements, and stakeholder alignment. Manages an AI and machine learning personalization squad developing recommendation, cross-sell, and upsell models. Owns data management, analytics, experimentation, impact measurement, and business insights to support data-driven decisions. The role bridges business, technology, and data teams across regional environments.

Own end-to-end delivery of complex AI client projects, translating evolving requirements into project scopes, delivery plans, and successful human data programs. Manage client relationships, cross-functional coordination, risks, quality, timelines, budgets, profitability, and delivery metrics. Partner with Sales, Operations, Product, and Engineering while improving scalable processes for AI project execution.

Lead commercial, privacy, and product legal functions for a cloud technology company. Draft and negotiate enterprise SaaS, partnership, reseller, and other technology agreements; advise on global privacy, data protection, AI, intellectual property, and product development; build legal playbooks and scalable contracting processes; and partner with Sales, Product, Engineering, Security, Compliance, and executive teams. Manage, mentor, and develop legal professionals while overseeing complex transactions and regulatory risk.

Owns the design, development, deployment, and support of Snowflake data models, Streamlit applications, BI reports, and AI-powered data products. Integrates machine learning models, RAG endpoints, and agentic LLM services into reliable user-facing tools. Partners with data scientists, engineers, and go-to-market stakeholders to translate business needs into trustworthy analytics solutions for a B2B SaaS organization.