Top Machine Learning Jobs in Ecatepec

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.

Own global fraud detection strategy across markets and fraud vectors. Lead fraud infrastructure, rules, machine learning models, AI automation, risk signals, case management, model governance, and performance monitoring. Identify emerging threats and improve detection through data analysis, automation, and cross-functional collaboration. Establish KPIs, ensure explainability and human oversight, and scale 24/7 fraud coverage across regional operations.

Source and develop data partnerships for AI training use cases. Translate customer needs into sourcing strategies, identify and qualify data providers, evaluate provenance and rights, coordinate diligence and samples, structure commercial agreements, and move partners through contracting, activation, and handoff. The role also builds scalable sourcing workflows, maintains partner pipelines, supports customer deals, and provides market intelligence across commercial, technical, legal, product, and operations teams.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Lead Protege’s DataLab research strategy and team, focusing on AI training data quality, model evaluation, synthetic data, privacy-preserving methodologies, and benchmarking systems. Partner with Product, Engineering, GTM, customers, and leadership to translate ambiguous technical questions into experiments, frameworks, and actionable recommendations. Establish rigorous, reproducible research processes, publish findings, influence product and customer strategy, recruit researchers, and represent the company across the AI ecosystem.

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.

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.

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.

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 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.

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.

Design, train, deploy, and monitor NLP and LLM-based machine learning models for an AI-powered risk intelligence platform. Build classification, entity extraction, clustering, semantic search, RAG, and agentic workflows; productionize scalable ML services; create evaluation frameworks; manage model quality, bias, drift, and retraining; and collaborate with data engineering and US-based product teams on multilingual, high-volume text data.

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.

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.

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.