Top Machine Learning Jobs in Mexico City

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.

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.

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.

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.

Designs and implements AI solutions, automation tools, models, and machine learning pipelines to improve Platts workflows. Collaborates with News and Price Reporting teams to identify AI opportunities, leads proofs of concept, trains cross-functional users, and promotes consistent global AI practices. The role requires generative AI prompt development, NLP knowledge, Python scripting, change management, communication, and the ability to translate technical concepts into practical business applications.

Own product strategy, priorities, roadmaps, backlogs, and value delivery for AI products. Partner with software development, data science, architecture, and product teams to translate business needs into features, user stories, and acceptance criteria. Drive Agile ceremonies, stakeholder alignment, technical direction, customer-focused innovation, delivery validation, and product adoption while leveraging generative AI, agentic systems, and machine learning.

Owns core backend computation services that produce correct, reproducible, traceable, and auditable financial results. Designs configuration-driven architectures, data-intensive services, and set-based warehouse computations. Requires strong Python, SQL, data modeling, system design, reliability, isolation, and testing expertise. Provides technical leadership through design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Leads the architecture, development, and deployment of AI-powered enterprise product capabilities, including generative AI, LLM applications, agentic systems, and intelligent automation. Collaborates with software engineering, data science, and product teams to transform emerging AI technologies into scalable, secure, observable, production-ready solutions. Provides technical mentorship, evaluates AI frameworks, manages project deliverables, and guides junior developers.

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.

Builds and maintains scalable batch and streaming data pipelines across on-premises and cloud environments. Designs data models, data flows, APIs, integrations, and access methods for reporting, dashboards, operational use, and data science. Translates business requirements into technical solutions, manages code integration, applies data governance, cleanses data, and supports complex project planning and delivery. Works independently across multiple dependencies and provides coaching to junior associates.

Leads PayJoy’s global Lock Operations function, developing strategy, takedown tooling, and operational processes to reduce lock exploit losses. Coordinates fraud, risk, engineering, and legal stakeholders; measures disruption and value protected; provides technical intelligence to Fraud; and manages two specialists and an analyst while handling high-impact takedowns.

Leads data engineering and ETL/ELT development using Databricks, Azure Data Factory, SQL, and Python. Develops and optimizes data pipelines, with preferred experience in Generative AI, machine learning data projects, Power BI, PySpark, Delta Lake, Azure Data Lake Storage, CI/CD, and data preparation for large language models, embeddings, vector databases, and retrieval-augmented generation.

Reviews real-time transactions and customer behavior flagged for fraud, investigates cases using internal policies and data analysis, and determines whether activity is fraudulent. The intern helps identify emerging fraud trends, improve detection rules and machine learning models, maintain case-resolution SLAs, and handle escalations. The role collaborates with analysts, operations specialists, data scientists, engineers, and support teams while contributing to fraud-prevention processes and tools.

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.

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.

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.